Communicating big data in the

healthcare industry

Master’s thesis

Author: María Castaño Martínez & Elizabeth Johnson

Supervisor: Selcen Öztürkcan

Examiner: Richard Afriyie Owusu

Term: VT20

Subject: International Business Strategy

Level: Master

Course code: 5FE40E

Abstract

In recent years nearly every aspect of how we function as a society has

transformed from analogue to digital. This has spurred extraordinary change

and acted as a catalyst for technology innovation, as well as big data

generation. Big data is characterized by its constantly growing volume, wide

variety, high velocity, and powerful veracity. With the emergence of COVID-

19, the global pandemic has demonstrated the profound impact, and often

dangerous consequences, when communicating health information derived

from data. Healthcare companies have access to enormous data assets, yet

communicating information from their data sources is complex as they also

operate in one of the most highly regulated business environments where data

privacy and legal requirements vary significantly from one country to another.

The purpose of this study is to understand how global healthcare companies

communicate information derived from data to their internal and external

audiences. The research proposes a model for how marketing

communications, public relations, and internal communications practitioners

can address the challenges of utilizing data in communications in order to

advance organizational priorities and achieve business goals. The conceptual

framework is based on a closed-loop communication flow and includes an

encoding process specialized for incorporating big data into communications.

The results of the findings reveal tactical communication strategies, as well as

organizational and managerial practices that can position practitioners best for

communicating big data. The study concludes by proposing recommendations

for future research, particularly from interdisciplinary scholars, to address the

research gaps.

Key words

Big data, Information and knowledge creation, Corporate communication,

Multinational corporations, International business, Healthcare.

Acknowledgments

Writing a Master’s thesis (during a global pandemic no less!) proved to be an

extraordinary learning experience. We were so lucky to have a strong network

of family, friends, colleagues, classmates, and professors from all over the

world who supported us throughout the process. We would like to express our

thanks and sincere appreciation.

First, we would like to thank our thesis advisor, Dr. Selcen Öztürkcan, who

was a bright beacon of light and guidance throughout the entire thesis writing

process. She recognized the value in our interdisciplinary topic from the very

beginning and helped us navigate the murky complexity of a topic that touches

so many dimensions of business, health care, technology, and corporate

communications. Selcen provided insightful critiques, challenged us to

consider multiple perspectives, and ultimately brought out the best in us as

students. Notably, she also made sure we prioritized our health and wellbeing.

We also appreciated that during a very scary time in world history Selcen

offered the ultimate comfort: inviting her cat to join our Zoom advisor meeting

sessions.

We would also like to thank Dr. Amy Leval, who inspired this study. Elizabeth

worked with her in the fall of 2019 and watched Amy’s team transform highly

complex scientific data into communications that made a real world impact.

Though it is still plausible that this is simply magic innate within Amy, we are

pleased to share the findings of this thesis which suggest it is possible to

replicate and cultivate her ingenuity within other professionals and teams in

the healthcare industry. Many months before COVID-19 emerged and it

became inescapably clear that there are severe implications for poorly handled

big data communications, Amy helped us identify this pertinent and timely

topic. She is not only a brilliant epidemiologist, but also a champion for

women in their academic pursuits and careers. We are lucky to know her and

grateful for the wisdom she shared with us.

Additionally, we are so appreciative for all our research participants. Thank

you for taking time out of your work days that are already busy during

“business-as-usual” but especially busy during an economic meltdown and

infectious disease outbreak. We are glad to have the opportunity to dignify the

important work you do in the healthcare industry by documenting the

incredible talent and skill it takes to effectively communicate information

derived from data. Your reflections, experiences, and contributions were

invaluable. It was extremely rewarding to write this thesis about the work you

do to meet medical needs, cure disease, and improve quality of life for people

around the world.

Sincerely,

María Castaño Martínez Elizabeth Ripley Johnson

Helsinki, Finland Stockholm, Sweden

22 May 2020

Table of contents 1 Introduction 1

1.1 Background 1

1.2 What is big data? 4

1.3 Digital transformation in the healthcare industry 6

1.4 Corporate communications and the healthcare industry 12

1.5 Theoretical problematization and research gap 16

1.6 Research questions 18

1.7 Purpose 19

1.8 Delimitations 19

2 Literature review 21

2.1 Journal scan 21

2.2 Big data 22

2.3 Data analytics 23

2.4 Data intelligence 24

2.5 Communication 26

2.6 Marketing communications 29

2.7 Public relations 32

2.8 Internal communications 34

2.9 Literature summary 36

2.10 Conceptual framework 37

3 Methodology 40

3.1 Research philosophy 40

3.2 Research approach and data collection 41

4 Empirical findings 48

4.1 Receiver and sender 48

4.2 Encoding 51

4.3 Message 56

4.4 Channel 60

4.5 Decoding 61

5 Analysis 63

5.1 Similarities in the existing literature 63

5.2 Differences in the existing literature 77

5.3 Summary of analysis 84

6 Conclusion 86

6.1 Answers to the research questions 86

6.2 Theoretical Implications 90

6.3 Managerial implications 92

6.4 Policy, social and/or sustainability implications 93

6.5 Limitations 94

6.6 Suggestions for further research 95

7 References 97

Appendices

Appendix A: Example of disinformation

Appendix B: Graph of Moore’s Law

Appendix C: Interview guide

Appendix D: Research participants

Appendix E: Conceptual framework

Appendix F: Research participants’ consent form

Appendix G: Research participants’ stakeholders

Appendix H: Amended conceptual framework

Figure and table index

Figure 1: Data-information-knowledge-wisdom pyramid

Figure 2: Sender-message-channel-receiver model of communication

Figure 3: Phases of strategic big data usage in corporate communication

Figure 4: Conceptual framework

Figure 5: Amended conceptual framework

Table 1: Systematization of corporate communication fields of activity

Table 2: Journal scan search keywords

Table 3: Research participant healthcare industry sectors

Table 4: Operationalization of interviews

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1 Introduction

Chapter 1 aims to provide an overview of the research topic: corporate

communications and big data within the context of the healthcare industry. The

following section includes a recent case that demonstrates the relevance of the topic

in today’s international business, public health, and digital communications landscape.

The chapter also provides background on the historical development of big data in

multinational corporations (MNCs), offers an overview of global healthcare markets,

illustrates how corporate communications functions differently in the healthcare

industry compared to other sectors of MNCs, and defines key concepts relevant to the

study. Additionally, the problem discussion explains why big data in the healthcare

industry is unique, how researchers have examined this topic in the past, and

demonstrates the lack of existing literature regarding how communicators can harness

big data to enhance communications that reach broad audiences, both internal to the

organization and externally to public stakeholders, and ultimately drive corporate

strategies forward to meet business goals and objectives. The introduction concludes

with the research questions, purpose, and delimitations.

1.1 Background

In January 2020, global media began reporting a mysterious virus was affecting

Wuhan, a city in central China. People were falling ill with pneumonia-like symptoms,

which scientists were calling a coronavirus (Barbaro, 2020). 17 years ago, Severe

Acute Respiratory Syndrome (SARS) broke out in China and resulted in a global

health crisis that infected more than 8,000 people and killed more than 800 (Barbaro,

2020). Public health analysts attribute part of the deadly spread of the virus was due

to the Chinese government both withholding information and perpetuating inaccurate

communication. Because of this lack of credible data, journalists were not able to

provide the public with factual and up-to-date news regarding how severe the virus

was, as well as whether people were getting the care they needed, taking appropriate

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precautions, and if the government was treating the situation as urgent (Barbaro,

2020).

This same phenomenon is seen in the case of the coronavirus today; but now the public

is increasingly turning to social media for information. This has sent technology

conglomerates, including Facebook, Google, and Twitter, scrambling to prevent a

surge of “half-truths and outright falsehoods” about the outbreak (Romm, 2020). This

carries immense risks, particularly in the fields of health and medicine, where the

posts, photos, and videos people share can shape how people think and their decisions

to seek and obtain much needed care (Romm, 2020). Public health authorities along

with these technology multinational companies have long struggled to curtail

dangerous health disinformation, deliberate misleading information, and

misinformation, false information that is spread regardless of whether there is an intent

to mislead, which includes posts, photos, and videos that scare people away from much

needed medical care. For example, the Chinese state-run media perpetuated

disinformation when they tweeted photos purporting to show a brand-new hospital in

Wuhan, but the images were actually stock photos from a company that sells modular

containers (see Appendix A). Likewise, the Facebook group, “Coronavirus Warning

Watch” is an example of a repository of misinformation where thousands of Facebook

users trade theories about the disease’s spread—in some cases suggesting it’s about

“population reduction”—along with links to articles peddling fake treatments (e.g.

“Oregano Oil Proves Effective Against Coronavirus” had been shared more than 2,000

times). Whether out of malice, fear, or misunderstanding, users can easily share and

reinforce disinformation and misinformation in real time, complicating the work of

doctors and government officials in the midst of a public health crisis (Romm, 2020).

Facebook claims to have responded by partnering with fact checking organizations

and leveraging its artificial intelligence system to search for misinformation, labeling

the inaccuracies in the posts while also lowering the posts’ rank in users’ daily feeds,

and ensuring it will not be included in recommendations or predictions when users are

searching within Facebook (Caron, 2019). Twitter started steering U.S. users

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searching for coronavirus related hashtags to the Centers for Disease Control and

Prevention. Google-owned YouTube said its algorithm prioritizes more credible

sources so people searching for news see authoritative sources first (Romm, 2020).

Despite these efforts, regulators and health professionals do not believe the tech giants

struck the right balance aiming to ensure digital debates do not cause real world harm.

This is further complicated by the fact that tech companies adamantly argue against

acting as “arbiters of truth” as Facebook chief executive, Mark Zuckerberg, has said

regarding deciding what users can say online (Romm, 2019).

In the midst of this, world stock markets were plunging, unemployment was

skyrocketing, and companies were going bankrupt as it became clear that this public

health crisis was morphing into the worst economic crisis since the Great Depression

(Goodman, 2020; Malkani and Torgerson, 2020). Although there is no way to measure

precisely how much misinformation exacerbated this debilitating economic ripple

effect, it stands to reason, from an international business perspective, the ways in

which coronavirus was communicated greatly impacted the financial and operational

performance of global companies.

This case is a timely demonstration of global challenges at the intersection of

international business, public health, and how big data is communicated. It reveals the

vast web of stakeholders who maintain competing priorities, including politicians,

government administrators, health authorities, news and media groups, multinational

corporations, and more who are communicating health, and healthcare related data

across the globe. As illustrated in this case, these actors, who may not have any

education or training in data analysis and the interpretation of scientific information,

are messaging information derived from COVID-19 data to their respective audiences.

This can lead to serious economic, health, and safety consequences.

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1.2 What is big data?

Multinational corporations have a long history of producing, storing, interpreting, and

subsequently, utilizing significant quantities of data. However, big data differs from

traditional corporate information and knowledge management due to its high volume,

velocity, veracity, and variety. Wiencierz and Röttger (2017) explain that big data

information assets consist of very large, complex, and variable amounts of data

(volume); concepts, technologies, and tools that are required for fast and systematic

storage, administration, and analysis of the heterogeneous data, in order to enable the

retrieval of the information within seconds (velocity); the measured data must be

reliable and accurate in order for corporations to make sound business decisions on

the basis of such data (veracity); and diverse in formats, structures, and semantics such

as text comments, videos, or data generated from wearables (variety). Subsequently,

these datasets are generated through computer and storage systems in a way that makes

these assets manageable and usable for organizations and individuals (Wiencierz and

Röttger, 2017). This understanding of big data, however, is a recent development,

despite the fact that its foundations have been evolving for decades, or even centuries.

Scholars cite the beginning of big data when society started analyzing and storing

information in physical documents and platforms, including the Library of Alexandria

as the largest data collection of the ancient world (López-Robles, 2019). At that time,

knowledge creation was seen as exclusively for academics (López-Robles, 2019).

Similarly, the emergence of statistics, which began as an academic discipline in 1660,

had a profound influence on data analysis as an application and tool for business

strategy (López-Robles, 2019). In 1865, the concept of business intelligence was

coined in the Encyclopedia of Commercial and Business Anecdotes, referring to

information analysis relevant to business from a structured and optimized approach

(López-Robles, 2019). This is acknowledged as the first application of data analysis

for commercial purposes. The concept of big data as it is known today emerged as a

result of the information and communication revolution, the development of the

Internet, and subsequent digital storage platforms (World Economic Forum, 2015). By

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the early 2000s, central processing unit (CPU) technologies were overwhelmed by

data storage. Thus, this IT crisis prompted the development of enhanced capacity,

speed, and intelligence of big data systems, which also brought down costs and

became more affordable for users (Russom, 2011).

This drastic increase of computing data was originally predicted by Gordon Moore,

co-founder of Intel, in 1965 (Sainz, 2015). He observed that the number of transistors

on integrated circuits doubles approximately every two years, and thus, this influenced

processing speed, products prices, storage capacity, and size of pixels in digital images

(see Appendix B) (Roser and Ritchie, 2015). As a result, the technology industry

adopted Moore's Law as a measurement of the product evolution and rate of

competition among tech-competitors. Moore's Law marked a significant societal

turning point from prohibitively expensive computing devices to affordable laptops,

and subsequently, smartphones (Sainz, 2015). This revolution occurred together with

the development of new Information and Communication Technologies (ICTs) as well

as rapidly expanding data collection and storage innovation. This is essential as big

data has reached exponential growth rates able to generate over two and a half

quintillion bytes daily (World Economic Forum, 2012) and forecasts project data

growth will increase by forty percent annually (United Nations, 2016). With the tools,

technology, and expertise required to collect, store, and process big data, companies

can finally transform data into useful information and trends. Thus, this can be used

to facilitate decision making within MNCs and is seen as one of the most powerful

assets within contemporary organizations (Roser and Ritchie, 2015; Sainz, 2015).

Big data, as with many innovations, however, can be a double-edged sword

(Buytendijk and Heiser, 2013). It brings the possibility of significant benefits by

allowing organizations to personalize their products and services on a massive scale;

it fuels new services and business models; and it can help mitigate business risks

(Buytendijk and Heiser, 2013). At the same time, there can be serious consequences

if consumer data is misused. This can be harmful for consumers, as well as for

organizations who can face reputational damage due to an inadequate understanding

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of data privacy issues. Governing and legislative institutions around the world are

starting to investigate data protections. In 2016, as a measure to address some of these

concerns, the European Union implemented the General Data Protection Regulation.

These regulatory measures lead to more consumer privacy protection, but it also

makes data gathering and use of personal data more challenging for the private sector

(GDPR.eu, 2016). Moreover, the consequences of not following GDPR protocols are

significant—fines up to 20 million euro or 4% of global total revenue of the preceding

year (whichever is greater) (GDPR.eu, 2016). As a result, big data can be both a

powerful organizational asset and an organizational threat if companies misuse it

(Buytendijk and Heiser, 2013).

1.3 Digital transformation in the healthcare industry

Global market overview

Big data is transforming many industries. However, it has the potential to make one

of the greatest impacts in the healthcare sector, particularly because every healthcare

company around the world generates, stores, and analyzes big data. Recently, one of

the main reasons for such a robust volume of data is that healthcare systems have

largely become digitized, by implementing electronic health records in hospitals and

clinics (Hersh, 2014). Patient medical records can include a wide variety of data

including clinical notes, lab reports, pathology images, radiology scans, and more

(Dash, et al., 2019). Healthcare companies also yield big data from medical

equipment utilization reports, online patient communities or forums, Internet of

Things (IoT) health and wellness-related devices, mobile applications, biomedical

and scientific research, clinical trials, nationalized patient registries, payer (e.g.

insurance companies) records, and more (Dash, et al., 2019; Luo, et al., 2016).

Therefore, not only is the volume of data difficult to manage, but the variety of data

formats from unstructured text in clinical notes to images to lab results to invoices or

financial-related data makes the storing, management, and analysis even more

complex. This requires both highly sophisticated technology and employee expertise

to identify useful and reliable information (Luo, et al., 2016). An integral component

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of big data being a functional tool and resource for companies is the ability to derive

meaning and interpret information from the raw datasets. This process of translating

data so that it becomes information has been thoroughly studied by information

science scholars. Data is not of use to key decision makers or practitioners if it is not

analyzed and interpreted, thereby becoming information (Kayyali, et al., 2013). Only

just recently has technology finally advanced to a degree where it is easier to not

only collect and store data, but also analyze it, and most importantly for the

healthcare industry, analyze datasets from multiple sources and in multiple formats

to create meaningful information and insights (Kayyali, et al., 2013).

By harnessing the power of biomedical and healthcare data, modern healthcare

organizations intend to revolutionize existing medical therapies and care systems

(Dash, et al., 2019). With data of this scale, variety, accuracy and availability,

companies are able to better equipped to conduct disease research, enhance hospital

administrative process automation, design early illness detection mechanisms, prevent

unnecessary doctor’s visits, develop disease prediction tools, discover new drug and

treatment options, personalize patient healthcare experiences, and more. In order to

understand the broader impact of big data in the healthcare industry, it is necessary to

acknowledge the complex ecosystem of the healthcare, life science, and biotechnology

market in which multinationals in these industries operate within. At present, the

market includes systems which aim to promote health, prevent disease, and provide

patient care (Dash, et al., 2019). Health and care systems are defined as broader than

hospitals and clinical environments, but also encompassing public health and social

care (European Union, 2018). The various components of a healthcare system are

deeply interrelated within the system network and thus, a variety of exchanges and

relationships exist. For example, primary care providers (e.g. physicians and

healthcare professionals) provide healthcare services to patients; insurance companies

provide insurance; reimbursement funds provide reimbursement; employers

contribute benefits; pharmaceutical companies create essential medicines; the

government is responsible for planning and managing healthcare infrastructure and

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regulatory matters; and the media plays a significant influencing role in the public

sphere (European Union, 2018).

Across the globe, spending on health and long-term care is steadily rising and expected

to continue (European Union, 2018). Today, aging populations, multi-morbidity (i.e.

multiple chronic conditions or illnesses), healthcare workforce shortages, increasing

preventable, non-communicable diseases caused by risk factors such as tobacco,

alcohol, and obesity, as well as the growing threat of infectious disease due to

antibiotic resistance and new, or re-emerging, pathogens (Trafton, 2020; European

Union, 2018) pose serious threats to healthcare systems across the globe. However,

this also creates unique opportunities for MNCs to contribute to reforms and

innovative solutions that address these challenges and create a more resilient,

accessible, and effective healthcare system.

Healthcare systems around the world see big data as a mechanism to navigate this

current landscape. For example, in Europe, the European Union is aggressively

pursuing digital solutions, which yield enormous amounts of data, for cost effective

health and care in order to increase the well-being of millions of citizens and radically

change how services are delivered to patients (European Union, 2018). The EU intends

to take action in three key areas: 1) Provide citizens secure access to and sharing of

healthcare data across borders; 2) Develop better data to advance research, disease

prevention, and personalized health and care; 3) Design digital tools for citizen

empowerment and person-centered care (European Union, 2018).

The EU acknowledges data as a key enabler for digital transformation and sees digital

tools as a way to translate scientific knowledge, help citizens remain in good health,

and ensure they do not turn into patients (European Union, 2018). The aim is that these

tools will also enable better use of healthcare data in research and innovation to

support personalized healthcare, better health interventions, and more effective health

and social systems. Because data is often not available to the patients, public health

authorities, medical professionals, or scientific researchers, the EU perceives this as a

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hinderance in delivering effective diagnosis, treatments, and personalized care

(European Union, 2018). Thus, health systems lack key information to optimize their

services, and providers find it hard to build economies of scale to offer efficient digital

health and care solutions and to support cross-border use of health services. Market

effective, and integrated approaches to disease prevention, care, and cures (European

Union, 2018). Today, the EU is developing high performance computing, data

analytics tools, and artificial intelligence to design and test new healthcare products to

provide faster diagnosis and better treatments. However, a key contingency in the

success of these initiatives is the availability of high quality, high volume data. The

EU is currently evaluating regulatory frameworks that will safeguard the rights of the

individual and society, as well as stimulate innovation (European Commission, 2018).

With Europe setting its sights on developing digital infrastructure and data driven

health systems, the United States is also driving digital health solutions forward by

targeting personalized healthcare. The United States has the largest healthcare system

in the world—11 percent of American workers are employed within the healthcare

sector (Bureau of Labor Statistics, 2020), accounts for 24 percent of government

spending (Center for Medicare & Medicaid Services, 2020), and is responsible for

17.7 percent of U.S. GDP (CMS.gov, 2018). Moreover, the U.S. healthcare industry

is expected to grow up to 7% annually from $103 billion in 2018 to $173 billion in

2026 (Lineaweaver, 2019). Despite this enormous economic engine, from a public

health perspective, the United States spends more than other countries without

obtaining better health outcomes (Papanicolas, et al., 2018).

Unlike Europe, the United States has a system that consists of private providers and

private insurance to pay for healthcare. As of 2018, 34 percent of Americans received

their healthcare via government insurance or direct public provision (Berchick, et al.,

2019). Without unified national healthcare infrastructure, patients have become active

participants in their healthcare by demanding transparency, convenience, access and

personalized products and services (Burrill, 2019). The U.S. home healthcare model,

based on telehealth, delivers quality remote care and has lowered cost, lowered

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readmission rates, and increased patient satisfaction rates (Lineaweaver, 2019).

Telehealth is one example where digital transformation in healthcare is being led by

the need for predictive and preventive care. The outcome is a digital health system

responding to consumer and patient demands that also results in cheaper, more precise

and less invasive treatments and therapies than traditional models (Burrill, 2019).

In comparison, to Europe and North America, the market environment in Asia and

Africa is less developed. In Asia, the health system is characterized as fragmented and

diverse with wide variations in healthcare policies and reimbursement systems across

Asia (Tham, et al., 2018). Practitioners and researchers are calling for an integrated

healthcare system with a collaborative and coordinated model of care across

stakeholders in healthcare settings. Less developed societies depend on development

assistance for health and on private insurance due to limited public assistance. Tham,

et al. (2018) acknowledged that in developing regions such as in Africa and Asia, an

integrated care model is meant to encourage benefits in sustainable health systems and

relieve the healthcare burden, they also recognize the crucial support from

international non-governmental organizations in developing and resource-limited

areas from Asia and Africa (Tham, et al., 2018).

In accordance, the World Health Organization's Regional Office for Africa, are:

improvement of the health security, strengthen national health systems, special

attention to health-related Sustainable Development Goals, address the social

determinants of health, and turn the WHO secretariat in Africa into a responsive and

results-driven organization (Pheage, 2017). Yet, technology innovation is disrupting

the future of healthcare in Africa as well, as an example, CareAI, an European

Commission project, is an artificial intelligence-powered computing system that

together with blockchain is able to diagnose infectious diseases, such as tuberculosis,

malaria, and typhoid fever within seconds. This “AI doctor” uses anonymous

distributed healthcare data to provide personalized health services to patients

anonymously, under useful contextual information, waning risks to the wider society.

However, African policymakers and overall health-related institutions and healthcare

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professionals will need to structure a new health framework to ensure patients privacy

and a secure global healthcare system; unnecessary to mention the main priority of

major developing regions, resources such as: accurate electricity infrastructure, clean

water system and available drugs (Ekekwe, 2018).

International business trends in healthcare

The synergy between healthcare and technology has risen a new spectrum of business

opportunities, top tech companies are integrating medical functions in order to obtain

health and wellbeing data. Google is striving to diagnose types of cancer as well as

heart attacks at early stages, while Apple is aiming at developing sensors to monitor

blood through the skin or glucose levels through tears (Todor and Anastasiu, 2018).

Likewise, Samsung has partnered with medical professionals at the University of

California to launch validation and commercialization of new sensors, algorithms and

digital health technologies (Todor and Anastasiu, 2018). This development is shifting

healthcare and biomedicine towards a coordinated management of the healthcare

system, which has a powerful potential impact on all its stakeholders: patients, medical

practitioners, hospital operators, pharma and clinical researchers and healthcare

insurers. However, the uneven development of healthcare on a global basis, as well as

the general public’s willingness to provide personal health data is complicating the

innovation process (Todor and Anastasiu, 2018). Likewise, regarding privacy and

security regulations protecting patient’s data, privacy policies are unevenly developed

around the world, which puts into consideration the legitimacy of the acquired

information. In both the private and public sector, innovative digital solutions can

improve health, boost quality of life, and enable more efficient ways of organizing and

delivering health and care services. For this to happen, they must be designed to meet

the needs of people and health systems and be thoughtfully implemented to suit the

local context (European Commission, 2018).

A significant international business concern for all is the many local, transnational,

and foreign laws and regulations healthcare MNCs’ products and services must

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maintain compliance with. Because laws in this area can vary from country to country,

this further complicates the potential for success with launching new products in new

markets. In the United States, the Food and Drug Administration (FDA) regulates the

launch of new medical devices and pharmaceutical drugs. It also regulates the

manufacturing and labeling and record keeping procedures for healthcare products

(Lamph, 2012). Receiving marketing approval for new healthcare products and drugs

from the U.S. FDA is expensive and time consuming. Likewise, in Europe,

Conformité Européenne (CE) marking indicates that a product meets the essential

requirements of all relevant European Medical Device Directives and is a legal

requirement to market a device in the European Union (Lamph, 2012). In India, the

Department of Health under India’s Ministry of Health and Family Welfare is

responsible for the regulation of medical devices (Lamph, 2012). In China, the State

Food and Drug Administration (SFDA) regulates the introduction of new medical

products in the Chinese market (Lamph, 2012). Thus, MNCs must comply with

regulations governing product standards, import restrictions, packaging and labeling

requirements, tariff regulations and tax requirements. Non-compliance with the

regulations and laws or failure to maintain, obtain or renew necessary licenses and

permits could ultimately impact the company’s operations and financial performance.

The global regulatory environment is a critical function of the success of any

healthcare company’s product marketing and sales strategy.

1.4 Corporate communications and the healthcare industry

Historically, corporate communication has fulfilled the critical role of disseminating

business information to a variety of internal and external stakeholders. All MNCs have

a variety of internal and external audiences they must communicate with; however,

healthcare is particularly complex. One of the ways in which corporate

communications is unique within the healthcare sector is due to both the volume and

the diverse range of stakeholders. A stakeholder is any person, or group of persons,

with which the company has, or wants to develop, a relationship (Dogramatzis, 2002).

Thus, the interconnection of stakeholders including employees, public and private

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payers, providers and suppliers, comprise the healthcare network ecosystem. Within

the ecosystem, there is a clear differentiation between internal and external

stakeholders. Internal audiences include every healthcare organization employee,

working directly or indirectly, as a business unit, committee, team, or union. Whereas

the external stakeholders are even more diverse and can be categorized into three

differentiated areas: Inputting, Mediators, and Consumers (Dogramatzis, 2002).

Dogramatzis (2002) indicates inputting audiences include: regulators, lawmakers,

politicians, reimbursements funds (e.g. payers and insurers), and suppliers. Audiences

who function as mediators are prescribers, scientific and medical key opinion leaders,

pharmacists, healthcare practitioners (e.g. doctors, nurses, etc.), and health system

administrators (Dogramatzis, 2002). Consumer audiences are perhaps most far-

reaching and include: patients, patient families or care takers, activists, the general

public, media, investors, competitors, and non-governmental organizations

(Dogramatzis, 2002). This means communicators in the healthcare industry must have

a thorough knowledge of each of these stakeholders including their distinct

characteristics and needs. Moreover, careful attention must be given to develop

relationship strategies, targeting messages effectively, and evaluating their

performance (Dogramatzis, 2002).

International business scholars often conceptualize corporate communications within

the marketing mix—falling in the promotion segment, which utilizes marketing tools

such as advertising, personal selling, sales promotion, and public relations to

communicate with customers (Kotler, 2000). However, because this study investigates

communication strategies beyond just customers (see Table 1) and encompasses both

internal and external communication, it is necessary to also seek out concepts and

definitions from communication science literature beyond traditional marketing. Van

Riel (1995, p. 25) defines corporate communication as “an instrument of management

by means of which all consciously used forms of internal and external communication

are harmonized as effectively and efficiently as possible, so as to create a favorable

basis for relationships with groups upon which the company is dependent.” Much like

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how data in the healthcare industry differs from the data a traditional MNC in another

industry would have access to (e.g. Pfizer has very different data assets than IKEA),

corporate communications in healthcare is similarly unique and differs from other

industries. This can be attributed to the enormous complexity of the healthcare

industry ecosystem. More so than any other industry, healthcare companies operate in

a heavily regulated environment where MNCs interact extensively with government

authorities, regulators, and politicians. Likewise, their stakeholders go far beyond the

individual consumer who buys their product or service (see Table 1). The complex

ecosystem of the healthcare industry is mirrored in each communication sphere’s key

stakeholders and audiences. This complex ecosystem of stakeholders subsequently is

mirrored within the healthcare company itself and its organizational structure.

Healthcare companies are heavily matrixed organizations, which is necessary to

operate with many stakeholders internally, as that is how they operate externally. As

a result, this is reflected in the corporate communications structure, which is also

matrixed (Dogramatzis, 2002).

Wiencierz and Röttger (2017) maintain that in order to understand the potential and

limitations of big data applications in the context of corporate communications it is

necessary to consider its three distinct and separate component spheres: marketing

communications, public relations, and internal communications. Marketing

communications is primarily responsible for corporate identity. It also drives brand,

customer, and product communications, but does so collaboratively with public

relations (Wiencierz and Röttger, 2017). Public relations is focused on reputation

management and external communication activations with the media, investors,

politicians, regulators, patient advocacy groups, and more (Wiencierz and Röttger,

2017). Internal communication is tasked with organizational communication from

business unit leaders to employees (Wiencierz and Röttger, 2017). Table 1 illustrates

the communications responsibilities per each component sphere. It is important to note

that although each component sphere has its own roles and responsibilities, the three

units are highly integrated and dependent on one another even when their

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communication responsibilities do not overlap (Van Riel & Fombrun, 2007). It is

essential that all three are aligned in order to communicate coherently and cohesively

to their many stakeholders (Van Riel & Fombrun, 2007).

Table 1. Systematization of corporate communication's fields of activity (Wiencierz and Röttger, 2017)

Internal communications Marketing communications Public relations

Corporate identity

Employee communication Brand communication

Management communication Customer communication

Product communication

Media relations

Investor relations/finance communications

Community relations

Public affairs/lobbying

Issue management

Crisis management

Corporate social responsibility communication

CEO communication

With the integration of digital channels and tools, a shift has occurred in how

companies reach their key stakeholders. Mainly, audiences are more accessible, so

thus companies have had to adjust their communications strategies, develop new ways

of messaging, and learn to leverage social networks and automated communication

platforms (Goodman, 2019; Wiencierz and Röttger, 2017). An outcome of utilizing

digital communication tools is that they are often built with mechanisms for tracking

information and gathering data (Goodman, 2019). Furthermore, as digital

organizational communication has broadened organizations’ stakeholders, companies

have had to adjust their strategies, developing new languages and narratives,

leveraging social networks and automated communications. These new business

practices have led business practitioners as well as academic researchers to study new

challenges and opportunities in digital communication, in order to understand and

theorize these changes, as well as perceive future trends and developments of new

applications (García-Orosa, 2019).

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1.5 Theoretical problematization and research gap

In the past, corporations have struggled to harness the power of big data because they

lacked data storage infrastructure as well as advanced analysis techniques and

methodologies for effectively analyzing the relevant data sets (Micu et al., 2011).

Today, companies have the hardware, software, and data processing tools, techniques,

as well as human capital expertise to store, organize, and interpret the data. This means

that companies are eager to communicate the information derived from data in order

to advance their business priorities and sustain competitiveness. The mechanics of

how to transform big data into information that can be communicated to many

stakeholders is sparse. Additionally, there is minimal existing literature on how

communicators leverage data into marketing communications, public relations, and

internal communications. Wiencierez and Röttger (2017) conducted a systematic

literature review to assess existing publications on the application of big data in

corporate communications and found the majority focus on marketing

communications, whereas the amount of research studies on public relations is

significantly low, and internal communication hardly exists. In addition, the

systematic literature review illustrated the lack of research in strategic big data usage

in corporate communication from a holistic and integrated perspective. They found

there were no studies assessing corporate communication as a whole investigating the

synergy of marketing communication, public relations, and internal communication

working altogether (Wiencierez and Röttger, 2017). García-Orosa (2019) agrees on

the limitations of single-channel studies and points out the need for new terms and

methods to study corporate communication in the context of big data.

Despite the lack of literature, the topic is timely and has pertinent implications for

practitioners as big data poses significant challenges to communicators who need to

synthesize the information and apply the data in multiple channels and contexts to

meet business requirements. These concepts are made all the more complex when

attempting to communicate big data across diverse geographies and cultures within a

complex regulatory environment within the healthcare industry. Communications

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practitioners who are responsible for delivering information derived from big data are

responsible for not only developing messages with complex information, but also,

must ensure their interpretation and dissemination of the data is compliant with cross-

border legal protocols and global regulatory requirements of data privacy. Beyond the

field of communication science, there is also a dearth of management literature related

to big data. Top tier business journals, such as the Academy of Management Journal,

Strategic Management Journal, and Journal of International Business Studies, have

published minimal, if any, articles related to this topic. This further validates the

research gap this study is seeking to address and the need to position the business

imperatives and international management implications for big data communications.

Measuring what matters and translating big data into business planning and decision

making are key priorities for corporations and management teams (Loebbecke and

Picot, 2015). Likewise, communications and big data is a significant international

business challenge. Effective communication of complex data enables strategic

decision making and enhances market positioning, which is necessary for MNCs to

sustain global competitiveness. Additionally, a research gap exists regarding the

intersection of communication and international business and big data. Current

organizational communication and international business literature lacks research

reflecting the intersection of these competencies. Few studies bring together

previously disparate streams of work in the fields of communication science and

information systems with respect to big data applications in corporate communication.

This complexity is intensified when it comes to international business, since

international performance and international information behaviors are characterized

by a greater diversity of elements, where additional variables and a higher level of

dynamism is present unlike it happens in domestic markets (Leonidou and

Theodosiou, 2004). Likewise, the healthcare sector is particularly relevant from an

international business strategy lens as every person, in every region of the world, is a

consumer of healthcare products and services. Big data offers healthcare MNCs the

opportunity to better understand this enormous customer base, develop effective

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communication strategies for reaching each of their audiences, and subsequently

enhance competitiveness and future growth strategies. However, empirical findings

are currently not addressing this phenomenon.

Ultimately, communicating effectively to customers and employees is one critical

mechanism for how companies achieve business objectives. The introduction of big

data offers communicators a new advantage and device to drive business priorities

forward. Multinational corporations have always managed large flows of information

and data. Similarly, corporate communications practitioners in the life science and

healthcare industries have always needed to message highly technical and scientific

information to internal and external stakeholders. However, neither have ever been

required to manage data at the enormous volume, veracity, variety, and velocity as big

data offers today. Thus, it is necessary to examine how this impacts the way MNCs

are communicating. As big data disrupts traditional business operations, how does it

subsequently affect corporate communications? Are the challenges communicators

face when utilizing information derived from big data unique? If so, what are the

challenges in utilizing big data in communications compared to technical information

of the past? Does big data allow communicators to communicate with their myriad of

internal and external stakeholders more effectively? Additionally, recognizing that

most communication practitioners are trained in the field of communications, not data

analytics, clinical research, or science in general, how do they ensure accuracy in

translating highly technical data? The answers to these questions are not found in

existing literature.

1.6 Research questions

• RQ1. How are multinational healthcare corporations communicating

information derived from big data to internal and external stakeholders?

• RQ2. What challenges do communicators in the healthcare industry face when

utilizing big data?

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1.7 Purpose

The purpose of the study is to understand how healthcare MNCs communicate

information derived from big data. Healthcare companies have access to enormous

volumes of data assets, yet they also operate in one of the most highly regulated

business environments where data privacy and legal requirements vary significantly

from one country to another. Thus, this sector offers a fruitful environment to study

big data-related communications from an international business perspective.

Additionally, this study is pertinent from the communications discipline perspective

because compared to other sectors, healthcare companies must communicate with

significantly larger audience which pose unique challenges.

The aim of the study is to examine how big data impacts traditional corporate

communications strategies reaching both internal and external audiences and how

global healthcare companies are communicating big data across marketing

communications, public relations, and internal communications to advance their

strategic business objectives. In order to address the gaps in existing literature, the

study intentionally seeks to understand the convergence of these three prongs of

corporate communication together rather than examine one discipline’s use of big data

independently. The study also seeks to understand what tools or methods

communicators are utilizing to engage both key internal and external stakeholders with

information garnered from data sources around the world to facilitate strategic

decision making, spur innovation, enhance competitiveness, and achieve business

goals.

1.8 Delimitations

The focus of the study will not include an assessment or review of tactics and methods

for storing, managing, or processing data. The intent is to understand how the data is

used after data experts, analysts, or scientists have synthesized and evaluated the data

so it results in information. This study examines the application of this information,

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and particularly, how non-technical business practitioners utilize the data in

communication strategies. The literature review was tailored to address the scope of

the study. For example, because big data is a relatively recent phenomenon, and

rapidly changing discipline, the literature review was limited to studies published

within the past 10 years. The topic was further narrowed to one industry, healthcare,

and one corporate function, communications.

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2 Literature review

The intent of Chapter 2 is to present existing scientific theories on big data, corporate

communications, and international business in the healthcare industry. Examining

existing studies and prior scholarly contributions subsequently informed the design of

the conceptual framework. The quality of the literature review was maintained by the

5C criteria: concise, clear, critical, convincing, and contributative (Callahan, 2014).

To follow the 5C criteria, the authors developed a critical review procedure comprised

of three analytical points: 1) methodology, 2) theory, and 3) key findings.

Additionally, in order to ensure relevancy, the authors tailored the journal scan to

review only the past 10 years (2010-2020) of publications on the topic.

2.1 Journal scan

To conduct the review of literature, the authors drew upon the Scimago Journal

Ranking list, which measures scientific influence of scholarly journals by accounting

for both the number of citations received by a journal and the importance or prestige

of the journals where such citations come from, to select the top tier management and

international business journals. The authors searched using a variety of relevant

keywords (see Table 2), however, a notable discovery was that the highest ranked

journals in this discipline, including the Academy of Management Journal, Strategic

Management Journal, and Journal of International Business Management, had

minimal, if any at all, articles addressing this topic. Thus, the authors expanded the

scan to review data science, healthcare, and communications journals.

Table 2. Journal scan search keywords

Big data Communications International business Healthcare

Big data, business

intelligence, data science,

data analytics,

information systems,

communication science.

Corporate

communication,

information and

knowledge creation,

MNCs.

International business,

international markets,

international performance.

Healthcare, healthcare

system, biomedicine,

wellbeing.

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2.2 Big data

As noted in Chapter 1, the study of big data is rapidly evolving and new insights,

theoretical contributions, and research is ongoing. Due to its recency, complexity, and

ability to be utilized across a myriad of sectors, big data definitions vary widely. As

big data continues to evolve, grow, and change, so too does the many interpretations

of what it is and how it is defined. Scholars have different definitions depending on

their field. For example, in the Journal of Information Science, Gupta and Rani (2018)

posit: “Big data refers to large datasets which require non-traditional scalable solutions

for data acquisition, storage, management, analysis, and visualization, aiming to

extract actionable insights having the potential to impact every aspect of health and

life.” Beyond applied engineering and information science disciplines,

communications scholars provide similar definitions. Wiencierz and Röttger (2017)

explain that big data information assets consist of very large, complex, and variable

amounts of data (volume); concepts, technologies, and tools that are required for fast

and systematic storage, administration, and analysis of the heterogeneous data, in

order to enable the retrieval of the information within seconds (velocity); the measured

data must be reliable and accurate in order for corporations to make sound business

decisions on the basis of such data (veracity); and diverse in formats, structures, and

semantics such as text comments, videos, or data generated from wearables (variety).

Subsequently, these datasets are generated through computer and storage systems in a

way that makes these assets manageable and usable for organizations and individuals

(Wiencierz and Röttger, 2017). Several authors maintain the importance of the three

(or, more recently, four) “Vs” which are key dimensions of big data: volume, velocity,

variety, and often, veracity (Russom, 2011; Wiencierz and Röttger, 2017; Mikalef, et

al., 2018). Volume refers to the size and complexity of big data compared to

conventional databases. Variety acknowledges the heterogeneity of data, regarding

formats, structures, and semantics, as texts or words, videos, images or the diversity

generated from the wide range of technological items. Velocity depicts the ability to

immediately store, administer and analyze heterogeneous data. Veracity alludes to the

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importance of being considered when making decisions based on big data analysis

(Wiencierz and Röttger, 2017).

Beyond scientific scholarship, the “Vs” have been adopted, and modified, by many

relevant industry institutions, such as the National Institute of Standards and

Technology, a laboratory within the United States Department of Commerce dedicated

to physical sciences, technology, engineering, and information systems, which define

big data as “consists of extensive datasets—primarily in the characteristics of volume,

variety, velocity, and/or variability—that require a scalable architecture for efficient

storage, manipulation, and analysis (National Institute of Standards and Technology,

2018).” Additionally, Gartner (2015), one of the world’s largest research and advisory

consultancies, defines big data as “high volume, high velocity, and/or high variety

information assets that demand cost effective, innovative forms of information

processing that enable enhanced insight, decision making, and process automation.”

Even in mainstream trade business publications big data definitions appear. For

example, Internet governance and regulation scholar, Viktor Mayer-Schönberger, and

technology journalist, Kenneth Cukier, defined big data, in their book, Big Data: A

Revolution That Will Transform How We Live, Work, and Think, as referring to things

one can do at a large scale that cannot be done at a smaller one, to extract new insights

or create new forms of value, in ways that change markets, organizations, the

relationship between citizens and government, and more (Mayer-Schönberger and

Cukier, 2013). Thus, beyond purely academic scholars’ interpretation of big data,

many other relevant actors from government institutions, to the MNCs, to the media

and journalists are defining and shaping the understanding of this phenomenon.

2.3 Data analytics

Beyond definitions and key characteristics of big data, much of the existing literature

also describes tools and methods that are applied in order to understand the meaning

of big data. The process by which big data is analyzed and organized into meaning, or

synthesized into information, is called data analytics (Mikalef, et al., 2018). This is an

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essential component of big data’s impact within an organization because, tactically,

raw data on its own is not useful to companies until it is transformed into information.

Friké (2009) defines information as relevant, usable, significant, meaningful,

processed data. This concept is illustrated in the data-information-knowledge-wisdom

(DIKW) pyramid (see Figure 1) which is derived from the information systems and

knowledge management discipline (Friké, 2009). As it is understood in this

framework, data is discrete facts without context. Rowley (2007) explains these facts

can be structured, unstructured or semi-structured data from a wide range of sources.

Data becomes information when it is put into context or given meaning through the

application of analysis. Thus, big data analytics harnesses analysis techniques,

technologies, systems, practices, methodologies and applications to organize,

structure, and critically analyze the data by identifying patterns and trends (Chen et

al., 2012).

Figure 1. Data-information-knowledge-wisdom (DIKW) pyramid (Rowley, 2007)

2.4 Data intelligence

The output of data analytics is subsequently data intelligence. Data intelligence is the

tool through which the analysis and the interpretation of information transform

information into knowledge, the next level of the DIKW pyramid (Rowley, 2007).

Data intelligence is an important concept in management literature because the intent

is to create strategic knowledge in order to make precise, high impact business

decisions and improve organizational decision making overall (Chen, et al., 2012;

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Saleem Sumbal, et al., 2017). Information derived from data can create valuable

knowledge, which ultimately promotes organizational competitive advantage (Saleem

Sumbal, et al., 2017). The objective of data intelligence is to improve business

performance by optimizing and enhancing opportunity identification, organizational

capabilities, trends forecasting, and eventually, decision making (López-Robles,

2019). However, several researchers remain uncertain of the degree of efficiency of

big data on the organizational decision-making process (Ransbotham, et al., 2016;

Elgendy and Elragal, 2016; Miah, et al., 2017).

One way in which scholars question big data’s ability to facilitate effective decision

making is attributed to organizational “data binges.” Bumblauskas, et al. (2017)

conceptualizes a data binge in instances where data is simply gathered without being

thoroughly or conscientiously handled, which then decreases data’s value as a tool for

decision-making. This contends data quality over quantity, when data lacks objective

analysis and knowledge craves action, the marginal value for organization is minimal

(Bumblauskas, et al., 2017). The conversion process from data, to information, to

knowledge, and to actionable knowledge, is essential (Bumblauskas, et al., 2017).

However, it is a complicated task when considering the interactions and relationships

across industries, organizations, international cultures, and legal parameters.

Additionally, Côrte-Real, et al., (2017) observed that organizational competitive

advantage and problem-solving capabilities diminishes with big data analytics as

accurate technology and ample organizational resources are essential for the analysis

to be effective and applicable. When analyzing big data application from a managerial

perspective, big data analytics has been based on the knowledge-based view and the

influence on dynamic capabilities, subsequently indicating a positive relationship

between information technology and organizational agility (Côrte-Real, et al., 2017).

However, knowledge and information are not always beneficial for businesses, since

it is not about how much organizations know, but rather how they use what they know

(Côrte-Real, et al., 2017).

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Organizations can apply what they know from data analytics is through effective

communications. One way to address this gap could be for both communicators and

data users to complete data literacy training in order to enhance the quality of the

collected data and information, and ultimately involve the whole organization through

effective communication practices based on an active bottom-up strategy to boost the

value of big data across the organization as a whole (Côrte-Real, et al., 2017).

2.5 Communication

In the academic and scientific literature, communications and data have historically

functioned in an interdisciplinary way. This is particularly evident in the information

management publications. For example, one of the cornerstones of today’s business

communications methodology, Shannon and Weaver’s Model of Communication

(1948) was developed first as a mathematical model. The model originally functioned

to explain technical communication around signal processing, or the exchange

between sender and receiver. This exchange is, on the most foundational level, the

basis of communication. Berlo (1960) amended the model so it became applicable

beyond the information technology discipline and into what is now the Sender-

Message-Channel-Receiver (SMCR) Model of Communication.

Figure 2. Sender-message-channel-receiver (SMCR) model of communication (Berlo,1960)

The model is structured as a loop where the communication process moves through

sender, encoding, message, channel, decoding, receiver, and feedback which is

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ultimately delivered back to the sender (see Figure 2). The sender is considered the

start of the communication process and ultimately encodes, creates, and distributes the

message to the receiver (Berlo, 1960). The sender is an individual, group, or

organization who initiates the communication development process (Sanchez, 1999).

Sanchez (1999) posits the sender is responsible for the success of the message. The

sender’s experiences, attitudes, knowledge, skill, perceptions, and culture influence

the message (Burnett and Dollar, 1989). The message construction process is called

encoding. Translating information into a message in the form of symbols that represent

ideas or concepts (Sanchez, 1999). The symbols can take on numerous forms such as

languages, words, images, or gestures. Symbols are used to encode ideas into

messages that a broader audience can understand (Sanchez, 1999).

The process of encoding involves the sender first making a decision on what needs to

be transmitted to the receiver (Burnett and Dollar, 1989). Part of this decision is

understanding as much as possible about the receiver (Sanchez, 1999). What

knowledge and assumptions does the receive already have? What information does

the receiver want from the sender? What language or symbols is the receiver familiar

with? Next, in order to transmit the message, the sender utilizes a communications

channel. The channel is the mechanism for delivering the message. Selecting an

appropriate channel is of equal importance as crafting the message itself (Burnett and

Dollar, 1989). If a sender relays a message through an inappropriate channel, its

message may not reach the right receivers (Sanchez, 1999). Selecting the right

channel will assist in the receiver understanding the full scope of the message. Sanchez

(1999) poses key questions to determine which channel is the best fit for a message:

Is the message urgent? Is immediate feedback required (i.e. bi-directional

communication)? Is documentation required? Is the content complicated,

controversial, or private? Is the message going to someone inside or outside the

organization? In some cases, more than one channel is required to effectively reach

the receiver.

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Once the appropriate channel(s) is selected, the message enters the decoding stage in

the communication process (Sanchez, 1999). At this point, the sender is no longer

active and the receiver is responsible for processing, examining, interpreting, and

assigning meaning to the message. The communication process is considered

successful if the receiver interprets the sender’s message as intended. However,

Sanchez (1999) emphasizes that there are many factors that impact the extent to which

the receiver will fully comprehend the message: how much the receiver already knows

about the topic, their receptivity to the message, the relationship and trust that exists

between the sender and receiver. All interpretations by the receiver are ultimately

influenced by their experiences, attitudes, knowledge, skills, perceptions, and culture

(similar to the sender’s relationship to the encoding process) (Burnett and Dollar,

1989).

The final phase of the process is feedback. After receiving a message, the receiver

responds (Berlo, 1960). The signal can take many forms: spoken comment, body

language/nonverbal cues, written message, an action, even no response at all, which

is, in a sense, a form of response (Bovee and Thill, 1992). including Further, feedback

is seen as highly important as it can reveal communication barriers: differences in

background, different interpretations of language, words, terminology, or phrases, and

differing emotional responses (Bovee and Thill, 1992).

With the integration of big data into the communications landscape, scholars have

established theoretical models to describe the process of how to make big data

manageable and useful for each of the component spheres of corporate

communication. Wiencierz and Röttger (2017) designed a four-stage model in order

to describe the process of incorporating big data into corporate communication (see

Figure 3).

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Figure 3. Four phases of strategic big data usage in corporate communication (Wiencierz & Röttger, 2017)

According to Wiencierz and Röttger (2017), Phase 1 articulates the communications

problems and objectives, as well as assesses whether big data can realistically address

these aims. Phase 2 ensures the reliability of the data by examining the data generation

process while also clarifying what type of data is used, how much data is available,

the accessibility of the data, how quickly it is being generated, and the authenticity

and integrity of the data. Phase 3 concerns the analysis of the big data. After collecting

the data, Phase 3 guides the analysis of the data. Finally, Phase 4 evaluates and

measures the value added by big data. Throughout Phases 2-4 the communications

professional will also be seeking to obtain all relevant stakeholder buy-in and

acceptance (Wiencierz and Röttger, 2017).

An essential element of Wiencierz and Röttger (2017) theoretical contributions is in

order to understand the potential and limitations of big data applications in the context

of corporate communications, it is necessary to consider its three distinct and separate

component spheres: marketing communications, public relations, and internal

communications.

2.6 Marketing communications

Contextualizing the healthcare system within the big data revolution, and analyzing

the marketing decision-making process in healthcare organizations, results in

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controversy among academics. Whereas some perceive clear problems regarding big

data management and high quality information (Aula, 2019; Bates, 2018), others

suggest that this will revolutionize the industry due to the value of the data over

volume (Agarwal, et al., 2020). Yet, this approach is primarily limited to the marketing

discipline, where new policies are encouraging patients to be empowered consumers,

whose preferences and experiences are being considered, rather than a not-distinctive

healthcare good/service receiver. Further, this trend is limited to the North American

context, and eventually supports the wide range of different healthcare systems around

the globe and the complexities that taking an international approach would face

(Agarwal, et al., 2020).

Therefore, the reality from a global perspective is that big data in the healthcare

industry is yielding controversy due to the urgency for reconfiguration of health and

biomedical data infrastructures and regulations at national levels, implying

coordinated measures that aims to the creation of an open database, generated by the

public and private sector and the civil society in order to provide a benefit for the

society through innovation and commitment (Aula, 2019). The analysis,

decontextualization and recontextualization of data is crucial in order for big data to

create knowledge and information (Leonelli, 2014), which strengthen the

interdependence of big data to its spatial and temporal context (Aula, 2019). Further,

big data challenges can be originated by the unstructured pieces of information across

different contexts, but in the health and biomedicine field, also by legitimate reasons

in order to protect individual’s privacy or national interests (Bates, 2018).

Healthcare data privacy is acknowledged in almost all countries, all institutions

collecting patients’ confidential data must adhere to the Privacy Rule and must make

sure its compliance (De la Torre et al., 2017). Awareness that possible threats exists

as internal, intermediary or external agents, and that no perfect security system exists,

as they must be adapted to the environment and requirements of that circumstances,

yet an insight of potential actions to prevent from non-ethical or misuse health data:

Accessibility to the confidential info, Electronic-based technology for secure storage,

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Back-up copies available, Secure encrypted info, A system that tracks security-related

occurrences, Physical media usage (De la Torre et al., 2017). Yet, it has been observed

that society shows little concern when sharing health data through health devices, apps

or social media, as individuals rarely pay attention to the “terms and conditions” where

it is expressed how their data is going to be handled and exploited by third-parties,

however, there exist high refusal from potential research participants or patients to

share their user-generated health data for scientific research purposes. The key to this,

though seemingly a contradiction, relies upon the conception of privacy by patients

and users. The same research found that, one of the major reasons for that

misconception was the significant disconnection between regulatory policies on health

data for research and healthcare, and the policies regulating corporate practices

(Ostherr, et al., 2017).

Traditional marketing activities in the healthcare industry have been characterized by

recognizing individual patients as enterprise customers. Emerging health technologies

and big data analytics are enabling to improve patients’ value and not be seen just as

mass consumers (Agarwal, et al., 2020). Yet in order to achieve a sustainable

healthcare system, then policy legislation, data protection jurisdiction, and open data

policies are essential in order to demonstrate the importance at the macro level and the

institutional approach on big data in the health industry (Aula, 2019), which in turn,

emphasizes the international challenge to unify or extrapolate data gathered in a

country where health systems strongly depends on the public sector, to other where

health is covered by private institutions.

The development of big data technology and analytics tools are providing marketers

large amount of opportunities in the healthcare industry. “Nature” data (e.g., genes,

hereditary factors) and “nurture” data (e.g., socioeconomic environment),

complemented with biological and medical information, behavioral data and

information about the environmental context, compile already a high volume of

datasets. The complexity arises when data is collected in a wide variety of formats:

structured formats (e.g., “likes” from the social media or checkboxes in an EHR) as

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well as in unstructured formats (e.g., clinical notes, online post from a patient support

group). Yet, advances on storage, processing and analysis of big data are underway,

which is reducing cost and further unlocking the potential of big data (Agarwal, et al.,

2020).

The types of data streams used in health analytics often include: Electronic Health

Records, genetic data, mobile devices and wearables, social media and online channels

are some of the most popular tools for marketing purposes exploitation (Agarwal, et

al., 2020). Despite its potential, healthcare consumers and patients are demonstrating

concerns towards due to lack of transparency and poor information about how and

why to use certain practices or treatment (Agarwal, et al., 2020). Additionally, there

is a risk in exploiting patients by gathering data about their healthcare experiences, or

asking for feedback, while they are facing serious health conditions. Furthermore,

once data has been collected and stored, there are various elements to consider when

analyzing the data and interpreting the information, in a fair and unbiased way. In

vulnerable populations, among minorities, low income areas, or rural regions where

accessing traditional healthcare is already problematic, integrating emerging

technologies could set barriers and exclude vulnerable populations from the healthcare

system, thus, excluding these minorities from AI-enabled care or EHR data, might

develop biased algorithms, which would end up into errors in diagnosis or treatment

(Agarwal, et al., 2020). Thus, more so than other sectors, there is considerable concern

about data ethics and privacy in the context of marketing communication (Agarwal, et

al., 2020).

2.7 Public relations

Public relationships or public relations (PR) encompasses a wide variety of

communication activities. The primary focus is on the organization's reputation in

order to influence its stakeholder’s opinion and behavior (Valjak and Draskovic,

2011). Due to the increased interest on healthcare industry, PR is playing a crucial role

in healthcare, facing many challenges, and offering many opportunities and

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developments in business but also in society (Valjak and Draskovic, 2011). One of the

key stakeholders for the healthcare industry is the patient, who organizations rely on

them to expand their market. However, lobbying with governments and health policy

makers is also considered as part of PR, as well as communicating the good they are

doing for society (Hasnmyer and Topic, 2015). Due to broad scope of the healthcare

industry, public communication management becomes complicated, as sometimes

different organizations or institutions within the industry are competing and providing

dis-coordinated and ineffective communication (Hasnmyer and Topic, 2015). In order

to cultivate a trusted reputation in the healthcare industry and build healthier societies,

transparency and honesty is required in communicating with the public. Healthcare

companies must also work collaboratively, and in sync as a whole industry, in order

to develop a unified communication strategy that addresses patient's needs in order to

provide value driven content, as well as to constantly educate society on the newest

treatments and developments (Hasnmeyer and Topic, 2015).

Technology development has also had an impact on public relationships. Today, PR

professionals do not have as much control over the content, and they no longer talk to

the public but they talk with the public, as digital platforms have increased

interactivity. Major shifts in communication vehicles and channels are creating great

changes in communication practices. For example, Twitter has gained more attention

than press conferences, as it is instantaneous and reaches a wider audience. Social

media has strongly impacted PR practices, as nearly anyone has become a “reporter,”

and can influence major news coverage or press release, fabricated information and

“fake news” have become routine (Clair and Mandler, 2019). Recent research shows

healthcare organizations, such as health insurers, medical device manufacturers,

pharmaceuticals companies and clinical healthcare providers have a significant

presence on social media platforms, and although hospitals are less active than the

other entities, users interact more frequently with them (Busto-Salinas, 2019). PR and

communications professionals are facing demanding times, and those who are best

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positioned to adapt to the new developments maintain traditional person-to-person,

trusted, and authentic relationships (Clair and Mandler, 2019).

2.8 Internal communications

Of the three corporate communications disciplines, the literature review revealed the

least about how internal communications functions within the healthcare industry.

Despite this, there exists an abundance of organizational communication research that

communication science scholars posit applies across industries. Studies indicate

effective internal communication is crucial for successful organizations as it impacts

the ability of managers to engage employees and achieve business objectives (Welch

and Jackson, 2007). Quirke (2012) described internal communications’ business

impact as the following: “In the information age, an organization’s assets include the

knowledge and interrelationships of its people. Its business is to take the input of

information, using the creative and intellectual assets of its people to process it in order

to produce value. Internal communication is the core process by which business can

create this value (p. 21).” Management and communication scholars have posited

internal communications as a powerful business function because it enables change,

fosters a collaborative organizational culture, and stimulates employee engagement

(Mazzei, 2014; Tkalac Verčič and Pološki Vokić, 2017; Zerfass and Viertmann, 2017;

Bailey, et al., 2017). Engagement is perceived as one of the most critical functions of

internal communications. Only engaged employees will be able to handle the complex

challenges of today’s volatile global, economic, and political environments (Zefrass,

et al., 2018). This is particularly applicable in the healthcare industry where innovation

is constant and requires employees to adapt to change frequently and quickly.

Welch and Jackson (2007) summarized the three primary functions of internal

communication: day-to-day management (employee relations), strategic (mission) and

project management (organizational development) (Welch and Jackson, 2007). Thus,

in both theory and practice, internal communication is critical to building relationships

with employees. Internal communication is recognized as important to organizations

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because open, effective managerial communication strategies have a crucial role in the

development of positive employee engagement (Bakker, et al., 2011; Bindl and

Parker, 2010; Saks, 2006). Employee engagement is “the degree to which an

individual is attentive and absorbed in the performance of their roles (Saks, 2006, p.

602).” Thus, employees’ knowledge and skills about both their jobs and the

organization provide them with the opportunity to become organizational advocates

with customers, who in turn can enhance the firm’s reputation (Gronstedt, 2000).

Internal communication enhances additional important bottom line outcomes for the

organization including increased productivity and profitability (Gallup, 2012).

Pounsford (2007) found that communication strategies such as storytelling, informal

communication, and coaching led to greater employee engagement, as well as

increased levels of trust in the organization and increased revenue due to greater

customer satisfaction. Likewise, Welch (2011) found employee engagement to be key

because it enables organizations to innovate and compete. In a highly competitive

globalized business environment, having engaged employees may be an essential in

competitive advantage (Macey and Schneider, 2008).

Tactically, successful internal communication relies on appropriate messages reaching

employees in formats that are considered to be both useful and tailored to them

(Welch, 2012). Understanding employee preferences for amount, channels, and types

of information have been explored in both qualitative and quantitative studies. Face-

to-face communication is understood to be the most valued approach for team and

project communication among peers, as well as electronic communication (White, et

al., 2010). Other studies corroborate this finding and also suggest that face-to-face and

email communication establish a sense of community in an organization (Stein, 2006).

Kelleher (2001) found varying communication preferences associated with different

work roles; managers preferring face-to-face communication, and technicians

favoring written communication. Overall, scholars note that employees prefer

different media for different sorts of information (Woodall, 2006).

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From a broader, program level perspective, Verčič and Zerfass (2016) found that the

highest performing, most successful communication departments have several shared

traits. For example, strong communications programs partner and collaborate more

closely with the executive board and other departments cross-functionally within the

organization; base their work on processes that involve significant amounts of

listening and research; and they produce more communications at the strategic level,

including overall communication and messaging strategies (Verčič and Zerfass, 2016).

2.9 Literature summary

The literature review presents a combination of recent and relevant theoretical ideas

regarding big data and communication. It also contextualizes big data communication

within the healthcare field. The majority of the existing literature focused on

marketing communication, public relations, or internal communication independently.

In examining the publications as they related to the healthcare industry, a commonality

was the acknowledgement that all functions of communications have a wide range of

audiences with varying degrees of data, and information derived from data, needs and

requirements. These disparate studies demonstrate a need for research from a holistic

communication approach. The literature also revealed integrating data into

communications in the healthcare industry is an extensive process primarily due to

data volume and varied stakeholders influencing in the communication flow. The data

must be generated, gathered, analyzed, interpreted, and packaged into a message

before it reaches the patient or final information consumer. Another essential learning

from the literature review is that buying and selling of consumer goods, or traditional

business practices as they relate to marketing, internal communication, or public

relations, do not always apply in the healthcare sector due to the complexity of the

market ecosystem and regulatory environment. Finally, the most prevalent theme in

the literature was that big data poses enormous opportunities and challenges for all

practitioners in the healthcare industry, however, communications professionals are

uniquely positioned to facilitate the transformation process from data to information

and deliver those insights to the audiences who need it most.

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2.10 Conceptual framework

Drawing upon the findings from the review of literature, the following conceptual

framework (see Figure 4 or Appendix E for full size model) was designed in order to

respond to the research questions. The framework integrates interdisciplinary theories

across communications and information science disciplines, and is positioned within

the unique business operating environment of the healthcare industry.

Figure 4. Conceptual framework (Source: Own figure based on literature review)

Structurally the framework leverages the Sender-Message-Channel-Receiver Model

of Communications (Shannon and Weaver, 1948; Berlo, 1960) which offers a

representation of the core communication flow. The intent of this model is to assure

that the sender’s message will be understood by the receiver (Sanchez, 1999). The

complexity of big data requires an even stronger focus on developing communications

in a way that the receiver can understand the sender’s message. Thus, this is a strong

foundation to begin from. Divergent from the original model, however, the conceptual

framework for this study repositions the receiver to the beginning of the

communication process because, as demonstrated by Dogramatzis (2002) what makes

communicating in the healthcare industry challenging is audience and stakeholder

management. This also addresses RQ2 which investigates the challenges associated

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with communicating big data in the healthcare industry. Since audience and

stakeholder management was one avenue in which challenges appeared in the existing

theory, it is necessary to position the receiver as a primary focal point.

A dotted line was also added between the receiver and the encoding process because

in some cases the big data that is synthesized for communications comes from the

audience or stakeholders themselves (e.g. patient data or medical records) (Hersh,

2014; Luo, et al., 2016; Dash, et al., 2019). This further emphasizes the need for the

receiver to be the first consideration in the communication flow when working with

big data within the healthcare industry. Once the receiver is identified, then the

communicator moves on to selecting the appropriate sender. Here the conceptual

framework draws on the Wiencierz and Röttger (2017) framework, Four phases of

strategic big data usage in corporate communication, which categorizes corporate

communication into three core areas: internal communication, marketing

communication, and public relations. After the sender is chosen, the model moves on

to the encoding process. Here, components of the Wiencierz and Röttger (2017)

framework is further integrated. This model emphasizes the importance of, prior to

crafting the message, establishing what is the communications problem that needs to

be solved and what is the objective? Further, what is the added value of utilizing big

data in this message? Can big data help solve the communications problem? And if it

can, by what means? Will big data help to describe, diagnose, predict, or make a

recommendation?

Finally, García-Orosa (2019) and Côrte-Real, et al. (2017), emphasized in the

literature the risks associated with utilizing big data, so it was important to include

guidelines in the conceptual framework. If big data is to be incorporated in the

message, then there are numerous critically important requirements to generating and

accessing the data itself around variety, veracity, volume, and velocity, including:

How is the data accessed and gathered? Is the use of this data compliant with legal and

regulatory requirements? How much data is required? When can the data be accessed?

What is the rate at which the data is being generated? Is the data source reliable? Is

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the analysis of the data reliable and accurate? Is the data applicable in more than one

international markets? After the encoding process, the communicator builds the

message, which is an important connection point to the information science literature

as this is where data transforms into information (Friké, 2009). Then, the information

is disseminated through appropriate communication channel(s), whereby the message

is decoded, or the information is by the receiver. This is where information can be

transitioned to knowledge on the data-information-knowledge-wisdom hierarchy.

Ultimately, because the model is structured as a loop, in the final phase, the receiver

provides feedback to the sender.

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3 Methodology

The methodology section outlines the structure and approach of the study. The study

aims at providing reliable and valid knowledge about a current global concern through

abductive scientific approach, combining a theoretical framework with empirical

results. The research design is based on an interpretive, descriptive, and qualitative

approach.

3.1 Research philosophy

Hermeneutism, or the interpretation of text, and interpretivist philosophy was utilized

in this study. Interpretative research is any type of research where the findings are not

derived from the statistical analysis of quantitative data (Corbin and Strauss, 1990).

Pizam and Mansfeld (2009) recognizes interpretivism as a philosophy that perceives

reality through multiple lenses and social constructions. The goal of the research is to

understand the phenomenon, not explain it. A perceived weakness in this research

philosophy is that predictions for the future are considered to be not as strong, in

comparison to positivist studies which provide clear and strong predictions (Pizam

and Mansfeld, 2009). However, for the purposes of this study, the intent is not to

predict how big data will be communicated in the healthcare industry in future, the

authors seek to understand how it is occurring today. Thus, this research philosophy

fits the aim of the study. In terms of data collection, interpretivism allows for a high

degree of interaction, cooperation, participation between the subject and the

researchers whereas the positivist approach requires rigid separation and maintains

absolutes (Pizam and Mansfeld, 2009).

Additionally, this study draws on the principles of constructivism. Remenyi, et al.

(2005) explains that constructivists maintain the world is socially constructed and

subjective. Researchers should try to understand what is happening and look at the

totality of each situation (Remenyi, et al., 2005). Small samples should be investigated

in depth. This method is relevant as an objectivism approach, which emphasizes facts,

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causality, and operationalized is not applicable to the research topic (Remenyi, et al.,

2005).

3.2 Research approach and data collection

The approach to collecting data is derived from the research questions and the aim of

the study itself. Although the discipline of big data and analytics is quantitative in

nature, this study intends to understand a qualitative dimension around interpretation

and dissemination of data through corporate communication channels. Qualitative

studies consist of several data collection methods in order to generate enough

information to understand the whole scope of the phenomenon. The primary data

collection technique will be semi-structured interviews and the secondary approach

consists of a thorough review of existing scientific articles and publications of relevant

studies.

Primary data collection

Primary data was collected in March-April 2020. 13 research participants were

interviewed and the interview ranged from 40 minutes to 70 minutes in length. Due to

the COVID-19 pandemic, all interviews were conducted virtually through a video

conferencing portal.

Sampling strategy

To identify initial research participants, the authors took a purposive sampling

approach. This technique requires the sample to meet a set of criteria in order to ensure

relevance within the study. Kumar (2011) explains that this approach involves the

researcher’s judgement in considering respondents who will provide the study with

the best information in order to achieve the research aims and objectives. Thus, this

type of sampling is useful for describing a phenomenon, or to gather information about

an unknown circumstance (Kumar, 2011). This approach supports the goals of this

study which aims to cultivate deeper understanding of communications and big data

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in the healthcare industry. Due to the global nature of this study, the authors set no

limitations on regional or geographic location of research participants. Participants

were selected based on the following criteria. The participants must:

• Work in a multinational corporation within the healthcare industry

• Have communications related responsibilities in their role (e.g. marketing,

public relations, or internal communications)

• Use big data in their communications activities

To find additional participants, the authors used a snowball approach to identify a

wider scope of participants to interview. Snowballing is the process of selecting

samples using a social network (Kumar, 2011). Browne (2005) describes snowballing

as a method of expanding the sample by asking one participant to recommend others

for interviewing. For this study, the authors requested the purposive sample

participants to give, at their discretion, the names and contact information of

communication professionals who fit within the research criteria. The interviewees are

expected to take part from MNCs within the healthcare industry.

The authors initially tailored the criteria to professionals who work within the field of

traditional corporate communication roles (e.g. marketing, public relations, and

internal communications). However, in the process of snowball sampling, many

recommended participants whose formal role or title was not part of a communication

function, but they were heavily involved in crafting communications related to big

data. Thus, the scope was expanded to meet the needs of the data gathering and provide

an accurate representation of how big data communications is functioning in the

healthcare industry at the time of the study.

Overview of research participants

13 research respondents participated in the data gathering interviews. A detailed table

of research participant information is presented in Appendix D. The research

participants worked in multiple different sectors of the healthcare industry, including

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pharmaceuticals, heathcare data analytics, medical devices, and consumer products

(see Table 3 for descriptions).

Table 3. Research participant healthcare industry sectors1

Pharmaceuticals Healthcare analytics Medical devices Consumer products

Discovers, develops,

produces, and markets

drugs for use as

medicines to be

administered to patients,

with the aim to cure

them, vaccinate them, or

alleviate them of

symptoms.

Analyze data to

transform information

into actionable insights.

Organize and access key

healthcare data assets,

ensure data is secure and

integrated, and apply

advanced analytics to

adapt to new care

models.

Devices intended to be

used for medical

purposes. Medical

device industry help

healthcare providers

diagnose and treat

patients and

subsequently help

patients overcome

sickness or disease and

improve their quality of

life.

Personal care consumer

products are used in

personal health and

hygiene.

Many healthcare MNCs operate within multiple sectors. Some respondents indicated

they started their career in pharmaceuticals and moved to medical devices within the

same company. Additionally, many have worked across sectors or at multiple

companies within one sector. Many companies span across these sectors as well. This

was particularly common in the pharmaceutical industry. Research participants who

were currently working for a pharmaceutical company had worked at other

pharmaceutical companies in the past. Likewise, respondents’ roles spanned the full

scope of corporate communication functions, including: marketing communications

(as it relates to brand and product communications), media relations and PR, as well

as organizational, leadership, and internal communication. Additionally, as mentioned

in the Sampling strategy section, multiple participants did not have a formal corporate

communications role or job title, however, they were strongly recommended as subject

matter experts in this area as they are heavily involved in crafting communications

related to big data. For example, one research participant is a formally trained

pharmacist and works as a medical affairs advisor at a pharmaceutical company.

1 Industry definitions from the Journal of Health Affairs (www.healthaffairs.org)

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Ultimately, this illuminated a noteworthy finding regarding how big data

communications is functioning in the healthcare industry. Often non-communications

experts are required to fulfill communications responsibilities when data is involved.

There is a need for interpretation of data beyond the resource, or skillset, availability

in traditional communication functions. Thus, employees outside of traditional

communications functions are required to develop messaging due to their degree of

data expertise and ability to interpret data. As a result, the research participant pool

includes a mix of technical experts (e.g. data scientists, statisticians, clinicians, and

epidemiologists) as well as communication experts. Additionally, research

participants ranged in career tenure—from only 1 year to 25 years of experience in the

healthcare industry. The authors interviewed junior employees as well as senior vice

president/director-level professionals. This was beneficial in gaining a clear picture

both tactically (how junior employees are crafting communication deliverables) and

strategically (how senior management is developing data communications strategy).

The majority of participants worked in healthcare and life sciences for their entire

careers. This may be explained by the highly technical and complex nature of the

industry. It may be difficult for those outside the industry to enter without specialized

knowledge or prior training. Notably, those with a long tenure in the healthcare

industry have worked across multiple product areas (e.g. oncology, women’s health,

neuroscience, etc.), business units (e.g. investor relations, research & development,

corporate social responsibility, etc.), and communications functions (e.g. internal

communications, public relations, and marketing communications). In terms of

educational backgrounds, the majority of the research participants with more than four

years of experience had advanced degrees or additional training certifications. There

was very minimal overlap in topics of study, which could indicate that the healthcare

industry requires, or seeks out, a diverse human capital talent pool.

Semi-structured interviews

The authors conducted interviews following a semi-structured interview format and

followed an interview guide of questions (see Appendix C). This approach allows the

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researchers to tailor questions to the respondent as the interview proceeds. Ritchie et

al. (2003) posits that reconstructing questions and allowing for follow up questions

achieves a more thorough and in-depth study. The questions in the interview guide

were operationalized in order to correspond with the conceptual framework and

research questions (see Table 4).

Table 4. Operationalization of interviews

Categories Interview

question(s) Connection to conceptual framework and research questions

Background &

current role/

responsibilities

1-5

The opening questions offer insight into the research participant’s

professional experience, educational background, and other credentials. It

also aims to contextualize the respondent’s role, which department they

belong to within the business, the business units they support, the

geographic region they are responsible for, and their core communications

responsibilities.

Channel 6

This question is tied to the Channel stage of the conceptual framework and

asks the respondent what types of communications they develop.

Additionally, this question seeks to answer research question #1 in order

to understand how communications with information derived from big data

are disseminated and delivered to receivers.

Message 7

This question investigates tools or methods for developing the Message

stage of the conceptual framework. Additionally, this question seeks to

answer research question #1 in order to understand how information

derived from big data is being messaged to receivers.

Receiver 8

The intent of this question is to understand the first stage of the conceptual

framework, the Receiver or audience(s) in which the research participant

communicates with and who their key stakeholders are.

Encoding

9-9a

These questions address Step 1 in the Encoding process and explain how

the research participant assesses the function of big data in addressing a

communication problem. It also aims to understand what their

communication objectives are when incorporating data into their messages.

10-12

These questions address Step 2-3 in the Encoding process and examine

how data is generated, what type of data is utilized, where datasets are

retrieved from, and considerations around variety, volume, velocity, and

veracity. The questions also examine the approaches the research

participant utilizes in analyzing data.

Decoding

13-14

This question addresses the Decoding process, which reflects on the

outcome of incorporating data into communications and how

communications are understood by the receiver. This section also focuses

on measuring impact and effectiveness of the communications.

Sender

15-18

These questions relate to the respondent’s role as the Sender and how they

functionally interact with the business. Additionally, these questions

answer research question #2 and focus on understanding the challenges

related to communicating data.

Confidentiality and research ethics

To ensure ethical research, the authors will make use of informed consent

(Groenewald, 2004). All research participants will sign a consent form at the time of

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the interview (see Appendix G). The informed consent agreement informed

participants they are participating in research; the purpose of the research (without

stating the central research question); the procedures of the research; the voluntary

nature of research participation; the participant’s right to stop the research at any time;

and the procedures used to protect confidentiality. In regards to data processing, the

study adheres to Article 5 of the Data Protection Ordinance of the European Union’s

General Data Protection Regulation. The audio recordings and transcripts are stored

in a secure file to protect personal data and confidentiality. The files do not include

personal identifiable information about the research participant. The participant’s

name was not coded or included in the labeling of the file. Rather, the file was coded

as Research Participant 1, Research Participant 2, etc. Additionally, the only data

collected was data that is necessary and relevant to the aims of the study.

Data analysis

With permission of the participants, all interviews were audio recorded. The

recordings were transcribed verbatim. Merriam and Tisdell (2015) define data analysis

as the process of making meaning through consolidating, reducing, and interpreting

collected data. This output is considered findings, which can be organized in

descriptive accounts, themes, or categories. Merriam and Tisdell (2015) defines the

process of data analysis as a method for finding answers to the research questions, and

subsequently, defined into categories or themes. The study’s conceptual framework

was also utilized as a mechanism for organizing findings. The aim was to synthesis

the information and outline consistent responses and detect recurring themes. Thus,

transcripts were read, reviewed, coded, re-read, and re-coded by both authors until a

chain of initial themes emerge (Orbe, 2000).

Research process and author’s contributions

Both authors shared an equal distribution of the workload (50%/50%) throughout the

research and writing of this thesis. Tactically, they also shared responsibilities in

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coordinating the logistics of identifying research participants, scheduling interviews,

testing technology to record and conduct the interviews. In order to expedite the

transcription process, Elizabeth Johnson transcribed the interview recordings as she is

a native English language speaker. For the methodology and literature review, the

authors divided the work based on topic to avoid redundancies. The authors wrote all

other chapters together in a shared document to facilitate discussion and incorporate

both perspectives. The authors did not work in-person or face-to-face, rather the entire

study was conducted, and the thesis was written, virtually due to the COVID-19

pandemic. However, the working relationship was strong, mutually supportive, and

highly collaborative despite not ideal circumstances.

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4 Empirical findings

The following section presents data and empirical findings gathered through

interviews with 13 professionals in the healthcare and life sciences industry who are

responsible for communicating data as part of their role at the time of the data

collection. Findings were coded, organized, and summarized based on the categories

within the conceptual framework (see Figure 4) in order to understand how

information derived from big data is being communicated and the challenges

practitioners face when trying to communicate big data within the healthcare industry.

Additional themes and recurring topics respondents highlighted, but could not be

categorized within the original framework, are also noted. As part of the

confidentiality measures for this study, research participants’ names and company

affiliations have been redacted. In order to qualify and provide relevant context while

maintaining anonymity, each respondent has an assigned label, which corresponds to

background information (job title, industry affiliation, type of company, business area,

geographic scope, years of experience in the healthcare industry, and education). Refer

to Appendix D for research participant information.

4.1 Receiver and sender

The empirical findings demonstrated an interdependent relationship between the

receiver and sender. Respondents explained the receiver determines the sender. This

was acknowledged as important because the sender determines which corporate

communications function (marketing communications, internal communications or

public relations) will lead the communication development process and drive the next

steps forward. Respondents indicated that this is because certain audiences are tied to

particular spheres of communication (e.g. employees, as an audience, are linked to

internal communications; the media is an audience connected to public relations). As

defined in the conceptual framework, the receiver is the audience, or stakeholders,

who receive the message or communication. Respondents cited a wide range of

stakeholders who are receivers of their communications (see Appendix G for a

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detailed list of respondent stakeholders). Internal stakeholders included the CEO, the

entire employee population, as well as more specifically those who work in finance,

health economics and market access research (HEMAR), medical affairs, commercial,

public affairs, hardware, software, and mechanical engineering, industrial design, user

experience design, manufacturing, research and development, legal, and quality

assurance. External stakeholders included customers/consumers, policy makers,

healthcare system administrators, payers, key opinion leaders in science and medicine,

media, the general public, patients, caretakers, patient family members, patient

advocacy organizations, and clinicians. Research participants were also asked about

the geographic scope for their role. Some respondents’ roles were global, regional

(Nordic, Europe, Middle East, Africa, and North America), or country-specific

(Sweden and the United States). In addition to specifying who their audience(s) is, the

respondents also explained how they assess the communication needs of their diverse

audiences in order to tailor messaging to effectively reach as many stakeholders as

possible. From an internal communications perspective, Respondent 1, who is a Senior

Business Insights Analyst at a pharmaceutical company, explained they think about

their audience in terms of how the information will be utilized after it has been

communicated.

“Depending on who I am giving the information will help me decide how I am going to frame the

information for them. I need to think about how they will read this information because they will be the

one using it later on. If the data is going to finance, I may leave part of it in Excel or the raw numbers,

because they will understand it. But if it is going to sales and commercial, I would translate using

graphics and visuals and add comments explaining the trends and clearly describe this is what is

driving this outcome.” -Respondent 1

Respondents also indicated that they refer to demographic information to create

unique communications strategies tailored to each stakeholder. Key factors that

influence the design include geographic region (e.g. language and intercultural

communication norms), level of education, experience within the healthcare field, and

more. This allows them to bifurcate the audience(s) even more narrowly in order to

communicate more directly. For example, internal communications research

participants explained that their employee audience range from a few hundred to many

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thousand employees, and often across multiple countries, regions, or locations. Some

employees are office-based, field-based (e.g. salesforce or medical device

technicians), or factory-based (e.g. manufacturing). Respondent 10, who is the Head

of Europe, Middle East, and Africa Communications at a pharmaceutical company

said, “I think what is important is thinking about their needs in terms of information

and communication style. Someone based at a manufacturing plant, who doesn’t work

in an office or in front of a computer all day, is going to have different information

and communication needs compared to a sales rep or someone who is based in an

office environment.” Other research participants spoke of effectively addressing

audiences based on organizational level or hierarchy within the company. Respondent

2, an internal communications specialist at a healthcare data analytics company,

explained, “For our VP of Technology, or the CEO, we can speak more freely and be

way more specific than we can with the general employee population.”

Additionally, research participants indicated external audiences must be bifurcated as

well. This is not only in order engage and reach their audiences more effectively but

to ensure they follow legal and international regulation protocols. Research

participants shared examples of needing to adapt marketing communications and

public relations to local audiences and markets. Respondent 8, a neuroscience medical

advisor at a pharmaceutical company, shared an example where marketing

communications had to be amended in each region due to laws related to

communicating pharmaceutical products.

“There is a rule in Europe where we can’t communicate pharmaceutical products to patients. If they

have a question, they can contact us and we can answer it, but we don’t do any proactive or direct to

consumer marketing communication. We can market it to clinicians and healthcare professionals, but

only after the drug is approved. Once the drug is prescribed, you can give the patient a leaflet which

has instructions for how to take the medicine. It can only be informative though. No claims about

efficacy or anything.” -Respondent 8

This example demonstrates the legal and regulatory considerations that

communicators in the healthcare industry face in trying to reach audiences across

regions. This requires communicators to fully understand the context (including laws

and regulations) their audiences exist within.

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4.2 Encoding

Step 1: Set the aim(s)

Before a message can be drafted, the content must be encoded. When working with

big data, this encoding process is thorough and in-depth. Respondents explained it is

necessary to first assess the purpose and function of data in the communications

strategy and/or output because this clarifies a company’s communications challenges

and objectives in order to determine the role of data in the communications output.

This step allows for, and assesses whether, data can address these aims. Research

participants emphasized before they even consider the communications aim, they first

establish how the communication itself supports business objectives and strategic

priorities of the organization. Respondent 1 said, “The goal is to answer the business

question. I wouldn’t collect any data unless the business felt they needed it. Everything

communications does is aligned to the overall strategy of the products and the

corporation.” Likewise, Respondent 4 said, “[The aim] always comes from internally

within the business and then we think how do we collect this data? How do we

translate this data and help the business?” Ultimately, research participants said

determining the data generation, collection, and incorporation into the communication

is secondary to ensuring the business aim is clear and well established. Respondents

said this is one of their central responsibilities when collaborating with the data

scientists or subject matter experts.

“I come to the conversation with data scientists trying to get them into the framework of the business

use case. What was the business problem? What are we trying to solve? Boiling it down to that. Then

you can start to get more into the weeds on the approach. Within the approach, it is nailing down how

is this actually used, where in the workflow is it used, at what frequency. This paints a clearer picture.

Then you can talk about the outcomes.” -Respondent 2

Most frequently, respondents said communication problems and objectives are often

derived from the following: day-to-day business operations, public health studies,

clinical trials, regulatory application submissions, and product development

milestones. Thus, the communication objective is frequently focused on informing

internal and external stakeholders about findings from scientific studies or status

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updates on product development, regulatory approvals, as well as the initiation of

applications for approvals.

“Usually [the company] has completed a period of research, the study has come to a defined milestone

or an end point and that necessitates a communication, either because it is public relations material

for the company, or because as a company we have committed to data transparency, which is our

commitment to sharing aspects of our data with the general public because in science it is important

for collective learning as well. When we do communicate, it is the outcome or a milestone within a

structured study.” - Respondent 12

Less tactically, and more generally, respondents said public relations and internal

communications objectives are often focused on demonstrating the success of the

company’s strategy and showing the progress the company is making. Respondent 10

said, “We need to show all of our stakeholders that we are delivering on our business

objectives and priorities that we have set out to do. Even when we are communicating

about the success of clinical trials or product launches, this is a way of showing our

overall strategy was successful and that we are progressing forward. This is the

reputation of the company.” Respondent 5 agreed that frequently the marketing

communications aim is to communicate the product portfolio, while the public

relations aim is to promote and safeguard the company brand. These findings also

illuminate the interconnected nature of internal and external communications.

Respondent 10 clarified this by saying, “Communicating externally on our successes

and developments is also a way of engaging our employees internally. Saying we are

sticking to our strategy and making progress and advancing our priorities and sharing

information and communicating about those milestones is important for employee

engagement and motivation.”

Step 2: Assess if big data can help to solve the communications problem

With a clear understanding of the communication objectives, respondents then

explained the important step of determining if big data can help to solve the

communication problem. The conceptual framework identified four core categories of

ways in which big data can be utilized to achieve communications goals: to describe,

diagnose, predict, or recommend action. These methods were reflected in the

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empirical findings in numerous ways. For example, Respondent 6, who is Director of

Product Communications at a pharmaceutical company explained how data can be

used to describe product efficacy in order to provide health safety information to the

general public:

“I worked on a ten-year clinical trial that had 25,000 patients, and what they were looking at was, is

[product name redacted] safe to take for patients with cardiovascular disease? This was a landmark

study and very high profile. The New York Times, the Wall Street Journal, all the network television

stations... getting the message out there that drug is [product name redacted] is safe. That was exciting.

That was helping people. They don’t have to worry is my blood pressure going to go up? Am I going to

have a heart attack if I take this? There’s a lot of health information we can offer through clinical trial

data in our product communications.” -Respondent 6

In many interviews, respondents explained data is multi-functional and spanned across

categories in order to meet communication objectives. Respondent 3 explained how

data allowed her to effectively communicate with her audience. In this instance, data

was used to describe the health of a particular population as well as provide

recommendations for how her client should address the health concerns the population

was facing. Likewise, a research participant who works in business intelligence and

market research at a pharmaceutical company said they leverage data to both describe

what is happening in the market, as well as diagnose or identify causal relationships

between market factors to explain market phenomenons. This subsequently leads to

communications aimed at providing forecasting (or predictions). Respondent 1

described the communications they create which include information derived from

data, are ultimately used to make business decisions.

“Often, we use data from market research to understand gaps and areas we don’t understand. In

business intelligence you collect a lot of information on our performance, how we are tracking

ourselves, but then how our competitors are performing and what is happening in the market. You need

to understand the data so we can track where we are and position ourselves for the future. We use the

data to help stakeholders internally make business decisions.” -Respondent 1

Conversely, there were also multiple examples cited where the communications

problem could not be solved with big data. Often this was because the data itself was

incomplete or not relevant to the audience. Respondents shared examples of situations

where they reached the data generation and encoding stage in the communications

development process and ultimately decided not to include data because the scientists

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or data analysts said they had concerns about the integrity of the data itself.

Respondents said gaps in the data, or missing data components, or the data they had

available was not applicable to the audience they were targeting flawed, incomplete,

or the experts were concerned about its accuracy. Moreover, respondents said that in

some circumstances the data itself was accurate, but the data scientists had concerns

about the accuracy of the algorithms or the tools they used to interpret the data.

Respondents emphasized that any concerns were immediately flagged and discussed

before moving forward in the communication development process. This was

understood as especially unique to the healthcare industry, which is highly sensitive

to any potential reputational risks. The industry is committed to scientific accuracy

and any threats to those operating standards are not tolerated. Respondents said that

any possibility that a communication would jeopardize the company’s credibility,

meant the project was put on pause until they had the right data or stopped altogether.

Step 3: Develop requirements for data generation

The final step in the encoding process involves completing the requirements for data

generation in order to procure the right data to meet the communication objective. This

means understanding how the data is accessed and gathered, legal and regulatory

requirements, including whether or not the data can be used in more than one

region/market, when the data can be accessed, what the rate at which the data is being

generated, and more. Research participants discussed the legal, regulatory, and

privacy-related elements in-depth. Respondent 3 said, “Before we even started a

project, we would ask if there was an NDA (non-disclosure agreement) already in

place. Do we have the right to use this data? We had to make sure there were proper

legal requirements in place before we start running data.”

In the conceptual framework, there is a dotted line connecting the receiver to the

encoding process because in some instances, datasets can come from the receiver. For

example, the receiver may be a patient diagnosed with a disease that the company is

trying to study. Thus, the patient group may provide medical records as a dataset for

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the company to use, analyze, and then communicate their findings back to the patient

group. Respondents underscored that this step in the communication development

process is done in collaboration with scientists or data experts who retrieve and

interpret the data. Research participants cited using the following data sources in their

communications: patient population registries, health insurance claims data, care

management and operations data, electronic medical records, pharmacy registries,

clinical trials, real world evidence and population health studies, as well as market

research and consumer behavior studies. Datasets varied based on geographic

location. For respondents based in Europe, they utilized data from patient population

registries, whereas this does not exist for populations in the United States and other

regions around the globe.

“The data that we use primarily in the Nordics is from the national healthcare registers. They are

available in all the Nordic countries. We have prescription drug registers, cancer registers, etc. You

can link the registers together and create big longitudinal cohorts. Sometimes electronic health record

data. We pool together data of millions of patient lives. They are longitudinal cohorts so they track

people over time. We can also link to events before birth and family medical history. Very few countries

have this type of total population registers and have had them for so long.” -Respondent 4

Although many respondents referred to generating data in-house via company owned

data sources, many also mentioned the need to access patient registries from national

healthcare systems or hire third party vendors to access specialized datasets. They

noted the process of assessing the variety, volume, velocity, and veracity of data via

third party agencies, in particularly, can be challenging and requires more vigilance in

vetting these outside sources. On the whole, it was clear from the interview findings

that every healthcare company is at different degrees of maturity when it comes to this

process. Some have more thorough and robust data generation protocols, and

procedures for vetting data sources than others. The research participants who work

in scientific research at healthcare MNCs or within healthcare data analytics

consulting had the most comprehensive and advanced data generation process.

Outside of data consulting and scientific research, the research participants who work

in traditional communications roles (marketing communications, public relations, and

internal communications) did not have the same degree of proactive planning or a

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well-established process for encoding. Often, they were not included in the data

generation process, and rather, asked to incorporate data into a communication after

the data had been collected and analyzed. This was noted as not always effective, and

that it is best to integrate communicators early on in the data generation process.

All respondents work for multinational health care companies, however, the degree to

which they work cross-borders varies significantly. This was cited as a challenge due

to the variances in regulatory and health care systems across countries. Thus, the way

communications teams’ message in one country or region might be ineffective, not

applicable, or potentially violates local laws and regulations in another country. For

this reason, communicators often operate in country specific silos, which can limit

interactivity between countries and regions. Respondents said that because they need

to be so focused on local requirements, they often do not prioritize staying connected

across other countries and regions, which they recognize can lead to a lack of

continuity in communications across the global organization. Respondent 11 said “I

need to get better at building my network in that way. I interface mostly with local and

corporate [global]. I would like to build a network in the company with communicators

in similar regional positions.”

4.3 Message

Although the encoding process was explained to be very extensive and requires

adherence to multiple steps in order to generate data to effectively communicate,

respondents agreed that the process of drafting the communication message is, by and

large, most difficult when working with big data. Respondents said that one of their

core responsibilities as communicators of the data is to dare to ask what the data

“actually” means. They said science and technical data is often so complex that it is

intimidating to ask experts what their findings mean in a practical sense. However,

this is essential in developing communications that reach audiences effectively.

Respondent 4 poses many questions in order to gather enough information to begin

outlining the message, “What do we know and don’t know? How can we put the

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research into context? What’s new here? What does this show? What are the

limitations? Are we communicating this in a balanced way? How are we simplifying

our findings in ways that people can understand but yet not remove any of the essential

parts from the study itself? That’s really important. We have to walk such a fine line.”

Being cautious not to change the meaning of the data by simplifying is key.

Additionally, respondents said that practicing not “overselling” what the data can or

cannot explain is also crucial.

For the message development process, all respondents emphasized the challenge

inherent in explaining data in “lay terms” or in common, simplified language.

Respondent 12 explained lay terms are frequently used in public relations: “For a press

release, there is usually a leadership quote or two. That is really the opportunity to

speak in lay terms about what the findings mean. Obviously, it is not an interpretation

because it has to be an accurate reflection around what the findings are.” All

respondents spoke to the task of translating highly complex data and information

derived from data and reformulating into more basic terminology. Respondent 6

explained, “If I don’t understand it, I can’t write about it. If I don’t understand it, then

the average person who doesn’t work in pharmaceuticals is not going to understand it

either. My whole job is taking complicated data and making it understandable and

making it relevant and ask why would anyone care about this? I just try to keep it

simple. Keep it short. Keep it clear. That’s my approach.” Other respondents said they

prioritize using terms, phrases, and language that their audiences know. It was

especially important to remove any technical jargon that would prevent the receiver

from understanding the message. In some instances, their stakeholders have the same

degree of expertise, technical knowledge, contextual awareness, and/or educational

background on the subject. Respondents said that a key factor contributing to success

in this translation process is getting comfortable with analytics, data, and science.

Respondent 6 said, “In order to do this job, you really need to spend a lot of time

understanding the science even if you aren’t a scientist.” Ultimately, communicating

scientific and health data requires discretion in maintaining accuracy and integrity of

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the findings and the complexity of the information. Essential to the translation process

in message development is collaborating with technical data experts. Respondents said

this can either make the message drafting process easier, or more complicated.

“…something that will be critical to set them up for success will be the ability to speak the language.

That often times sounds so basic that people rush over it. We are all human beings. We are all pretty

smart. It’s not about being smart or not. It’s about, do you understand this word to mean the same thing

as I do? Because that is not always the case.” -Respondent 7

Respondents said that this iterative drafting process involves multiple conversations.

They speak with the subject matter expert (e.g. scientist, data analyst, etc.) and try to

understand and retain as much as they can from that initial discussion. They then make

a first attempt at a draft of the message. Every research participant cited the necessity

of multiple drafts and iterating in the translation process and message

development. Respondent 3 said, “I say to [the data expert] this is what I think you

were trying to say and this is how I translated it. They will say you are pretty close,

just change this, this, and this. It definitely went through multiple iterations.”

Respondents said this iteration process gets easier and throughout the tenure of one’s

career in the healthcare industry because they build strong relationships with subject

matter experts and learn to ask right questions to get the answers they need to propel

the communication drafting process forward. Respondent 6 said, “I’m not a scientist.

I’m not a doctor. I’m not a statistician. But I have always tried to cultivate relationships

with those people in the company and have them explain [data] to me.” Multiple

respondents spoke about the importance of cultivating trusted relationships and

gaining their trust.

“The first thing I learned when I joined the industry in my twenties is that you find a couple of really

credible scientists in house, in the company, who know how to explain things. Those folks quickly

become your best friends. They are people who can help you to understand something that may be too

complicated for you to get. They are people who can help me if I need someone to talk to a journalist.

Or even someone to talk to employees at a townhall to explain something, they will have an anecdote

that they pull out that does a great job. You find those people. It’s a necessity from the beginning.” -

Respondent 12

They also mentioned that the longer they worked in the healthcare industry, the better

they have become at knowing when to reach out to other subject matter experts in

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other functions or business units who can provide input and ensure all internal

stakeholders were in alignment. Respondents indicated that inter-function and cross-

business unit collaboration was on-going, frequent, and essential. Respondent 5 said,

“Communications is in the middle of everything. We interact with everybody and then

once I get my materials developed then I have to get them approved by medical, legal,

regulatory, compliance. It’s very cross functional.” Additionally, respondents

described how important it is to work collaboratively within the component spheres

of corporate communications. Respondent 13 said, “I collaborate constantly with the

marketing communications and social media teams. We rely on each other a lot. We

share ideas and stories. The lines between our functions are so blurry. I think it’s for

the good of storytelling because we each have audiences we need to message to. And

it is good for all of us to understand how the story can be understood through different

lenses.”

A challenge to the message drafting process is technical, scientific, or data-related

practitioners’ lack of communications skills. However, they also said training for

traditional communications professionals is equally necessary. Not only do subject

matter experts need additional training, but communications practitioners need to

enhance their technical expertise to improve collaboration.

“We have something called Data Science University. It was meant to be internal talent development so

taking data scientists who are working on traditional analytics and getting them up to the next level.

This year we are really shifting to be about beginner levels, taking business leaders and product

managers, giving them the basic vocabulary and walking them through different use cases. Part of that

is helping them understand what data and analytics can help with and what it can’t help me. It’s not a

magic bullet. If you don’t have the right data to solve a problem, you can’t just run whatever you have

through a model and get out insights. We explain both possibilities and limitations.” -Respondent 9

Public relations research participants pointed out the challenges that emerge when

trying to communicate within the constraints of one of the most highly regulated

industries. However, they also commented that this offers “guard rails” and assures

consumers, clinicians, patients, and other essential stakeholders that the message they

receive from health care companies is transparent, honest, and only communicating

information that can be confirmed with facts and data. Respondent 12 summarized

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this sentiment, “We can’t do PR in our industry in the way that other industries can.

We are regulated so we can only be precise and accurate. Other companies aren’t held

to that standard. Companies can make claims that we can’t make. There is a lot of

misinformation. They can make claims that are not substantiated.” Marketing

communications professionals also emphasized the particular challenge in messaging

product information to patient audiences while fulfilling all regulatory requirements.

“The hardest part is communicating to the patient audience. When you get a prescription, it usually

comes with an insert that explains all the possible adverse side effects and directions on how to take it.

That is written in a very complex way and in very small font. If you look at the average health care

user, they are probably older, their eyes aren’t good. The industry is trying to figure out how to be

compliant within the regulatory structure and the regulations that govern us, but also make sure we

are helping our patients accurately interpret the data and information so they understand the benefits

and risks associated with the medicines they are taking and how to safely take their medicine. The

industry has done a lot. Everything from if you want to participate in a clinical trial, they have rewritten

the consent forms so patients understand what they are consenting to. I think it’s a 6th grade reading

level. There are industry standards is the US and Canada and Europe that guide how you should be

developing certain materials. They are very specific.” -Respondent 12

4.4 Channel

After the message is drafted, the channel for distribution is identified. Selecting the

best channel for the message, audience, and purpose of the communication is where

traditional communication experts found the communication development process the

most seamless. Conversely, respondents who are data or science-oriented struggled

with which channel to disseminate their message. As is consistent throughout all

stages of the conceptual framework, respondents said there are a myriad of regulatory

and legal requirements to be aware of when selecting channels for message delivery.

“There have been a couple of cases where employees on their personal LinkedIn pages promoted a

press release from the United States that speaks about an upcoming drug that is not yet approved in

our region and then the pharmaceutical company was fined. We have to be very careful about how we

mention product names in each country. That is critical.” -Respondent 5

Respondents indicated the following as channels they select from: videos posted to

online video-sharing platforms (e.g. YouTube, Vimeo, etc.), radio, press releases,

PowerPoint presentations, media interviews (television segments, print articles, online

articles, radio segments, podcast episodes, etc.), scientific publications, social media

posts, Intranet articles, company/employee meetings (in person, or virtually via video

live stream or audio dial in), email memos/announcements, newsletters, frequently

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asked question documents or instruction manuals/guides/brochures/leaflets, sales

training materials, and more.

4.5 Decoding

Once the message is disseminated through the communication channel, the

information is delivered to the recipient. In the delivery process, the recipient must

interpret, or decode, the information. Respondents indicated that this part of the

communications process is the most difficult to measure and challenging to know if

the receiver understands the message as the sender intended. In general, it is difficult

to measure the effectiveness of the communication. This is where the sender is able to

see the outcome of incorporating data into communications and how communications

are understood by the receiver. Respondents emphasized the importance of assessing

the value added by incorporating big data into communications. Did the data

ultimately meet the communication aim? Was the communication problem resolved?

There was a wide range of evaluation mechanisms and methods cited in the empirical

findings to measure the impact of the communication. From a scientific perspective,

some respondents cited publications and journal impact factors. For example, if an

article is published in the New England Journal of Medical, the Journal of American

Medical Association, the Lancet, is one way to assess impact. Respondents also

mentioned digital metrics and analytics as a way to understand reach and audience

engagement. Respondent 6 explained, “We get metrics from press releases to see how

many have opened it. We can track that. When we are working with digital campaigns

and Google AdWords campaigns. The beauty of working digitally you can see how

many views and conversions you have for a specific campaign.” There is so much data

from these digital platforms that many respondents said they have not begun to fully

optimize and utilize the information from these sources.

“Social and digital media is very data rich. You can tell how long people come to your website, what

do they click on, how long they spend on a page, how many people have read your tweet, how many

people have retweeted or posted a comment. It is so significant that we haven’t figured out what to do

with it all. For employees we look at the number of people who clicked on an email or participated in

a webcast. It’s also both quantitative and qualitative. Even on social media, we will look at are they an

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influencer? What is their reach? We will also look at the comments and see what is the tone of the

comments? In the olden days you used to count press impressions. If you were on the front page of the

New York Times and they sell to X million people, that means you got X number of impressions.

Nowadays we can dig a little deeper, in terms of the demographics of who reads an online publication

and figure out was this post then put on social media? If it was posted on social media by a journalist,

who retweeted it? Who liked it? There is a way to extend the data of what traditional metrics could give

us.”-Respondent 6

Another way respondents measured the impact of their communications was assessing

policy measures or changes in consumer behavior. Respondent 4 said, “If you use a

study to support health technology assessment to show policy makers whether or not

they should fund a medication in their population. This is a big deal because if they

don’t fund it then life expectancy is going to be shorter. But then you have to show

why it is worth it to them to actually bring this new innovation into their current health

system. That is high impact. Making sure patients can get access to medicine is what

is most important. Just because you come up with a new innovation, doesn’t mean it

is going to be taken up by the local health systems, you have to show that it is going

to bring some benefit versus the current standard of care and it is worth it to society

and patients for long term outcomes.”

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5 Analysis

The following section connects the empirical findings with theories from the existing

literature (detailed in the literature review in Chapter 2) in order to understand the

contributions and implications of the results. The analysis follows the structure of the

conceptual framework proposed by the authors, in order to prove its applicability to

the research context and also demonstrates areas where the empirical findings aligned

and diverged from the literature review. The authors coded and labeled each interview

transcript to collect similarities and differences between the research results and the

theoretical framework. Subsequently, the researchers tracked how often these key

themes appeared in the interviews in order to distinguish, both the most common

patterns and alternative practices that are lacking in the existing scholarly publications.

5.1 Similarities in the existing literature

Receiver

Audiences and stakeholders

Both the theory and the empirical results demonstrated digital transformation has

changed the way MNCs communicate with their stakeholders. This is seen as

especially transformative in how organizations are able to engage key stakeholders

and audiences (Goodman, 2019; Wiencierz and Röttger, 2017; García-Orosa, 2019).

With digital technology, a hybrid online ecosystem has emerged, which is based on

interactivity, or bi-directional (i.e. two way) communication practices, and this has

provided new access and an expanded audience(s) networks for MNCs. All 13

research participants upheld this notion and shared examples of ways that digital

technologies have opened access to new audiences in the healthcare industry. As the

conceptual framework demonstrates, different audiences and stakeholders are defined

as receivers of information, and at the same time, they can function as data sources

for the companies as well. As Dogramatzis (2002) indicated in the literature,

respondents similarly echoed the receivers specific to the healthcare industry: health-

related regulators, lawmakers and politicians, reimbursement funds, payers and

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insurers suppliers, prescribers, scientific and medical key opinion leaders,

pharmacists, health care practitioners, health system administrators, patients, patient’s

families and caretakers; activists, general public, media, investors, competitors, non-

governmental organizations, employees, contractors or temporary workers and board

of directors.

The literature suggested many complexities and challenges in regards to stakeholders’

relationships and corporate communication, particularly when operating

internationally. Because all of the research participants worked for a multinational

corporation, most of them also had cross-border, regional, or global responsibilities.

However, despite being a robust global industry, the health and biomedicine sector is

characterized also by local regulations because health systems are tied to national

policies that vary significantly from country to country. Aula (2019) and Bates (2018)

presented this approach from the external communication perspective, where they

found important barriers to big data utilization in the global healthcare sector due to

the diverse infrastructures and regulations at national levels across countries and

regions. Their findings indicated that coordinated measures and open databases would

facilitate better use of big data and enhance societal benefits through innovation and

commitment. Respondents supported this position by emphasizing that big data should

be “borderless” but that is not the case. Data has borders. It belongs to the country in

which it originated, so thus the same parameters apply as conducting business cross-

borders in any other operational function. Respondent 8 summarized the limitations

of big data when working in the global healthcare industry as it relates to stakeholder

management.

“We focus only in Sweden. But I still talk with medical affairs people in Norway, Finland, and Denmark.

Internally, we have the EMEA organization who creates materials we can use too. We share data and

best practices or general problems we have. The markets are pretty different. In Spain, the health care

system is different so how the pharma companies interact is different than what we are doing in Sweden.

Not everything is valid. You have to pick and choose what you want to use.” -Respondent 8

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As indicated in the conceptual framework, data sources can often originate from the

receiver which introduces the need for data governance. This concept is also relevant

to this stage of the conceptual framework as regulators, lawmakers, politicians, and

health system administrators are key receivers involved in data governance. As the

literature demonstrated, data governance receives special attention in the healthcare

field in order to safeguard patients' privacy (Bates, 2018). The goal of data governance

is to protect the receiver while also maximizing data value and minimizing data-related

risk for the organizations. Data users are not always cognizant of potential risks that

mismanaging or mishandling big data can lead to, and therefore, more and more data

regulation policies have been implemented by legislative bodies around the world

(Abraham et al., 2019). The literature called out patient confidentiality data

regulations, storage security systems as well as unethical or misuse health data

compliance forms as the most pressing data governance concerns for MNCs (De la

Torre et al., 2017). Research participants echoed these same concerns as top of mind

and very pressing within the business operating model. They also explained how the

external regulatory environment heavily influences internal protocols within

healthcare MNCs. Respondent 5 said, “We stay super compliant and obey the local

regulations but we also have our own internal rules as well which makes

[communicating] even more complicated.”

Although most respondents emphasized the struggle that comes with navigating their

internal and external policies, many also acknowledged these protocols as a

“beneficial barrier” that aims to diminish risks from data usage in the industry.

Respondent 6 explained, “Communications is in the middle of that, we interact with

everybody, and then once I get my materials developed then I have to get them

approved by medical, legal, regulatory, compliance…but I understand the legal and

regulatory reviews. They are trying to keep the company out of trouble, they are trying

to keep you from saying too much or from overstating things, or distorting things,

which is obviously very important. We always want to be honest, and we are. We are

honest and transparent about both bad data and good data.” In fact, some research

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participants acknowledged that this actually advances the credibility of their

communications. Data governance standards and regulatory restrictions ultimately

prevents misinformation or false information from reaching receivers. Healthcare

MNCs cannot make claims that are not substantiated with data and that data must

follow data governance protocols. As a result, data governance and regulatory

measures act as a mechanism to safeguard receivers but also ensure data accuracy in

the communications they receive.

Sender

Internal communication, marketing communication and public relations

The conceptual framework defined the senders as each component sphere of corporate

communications: internal communication, marketing and public relations. The

literature explained each of these functions and suggested best practices for how

communications should function internally within the organization. The empirical

data supported the literature that indicates implementing big data into corporate

communications leads to a more unified, holistic process within the business operating

structure, where data is gathered and analyzed across departments and functional

divisions, where shared trust and engagement enhance competitive advantages and

eventually the business performance (Akter, et al., 2019). Akter, et al. (2019) also

posited that the process of integrating big data within the organization brings many

challenges that can hinder its potential. Respondents shared examples of the

difficulties that come when working across departments or business units on

communications that utilize information derived from data, particularly the iterative

process that is required to ensure that the messaging is accurate. Respondent 9

provided a clear example of how communicators work internally, by engaging in

bidirectional and cross-functional communication, in order to maximize the value of

external communication:

“We all take a pass at the data ourselves and then bring everyone together to discuss what we found.

There is some back and forth where someone will say, “I think they could focus on diabetes because of

X, Y, and Z.” Then one of the analytics folks might say, “Oh but you didn’t think about this, which could

be impacting the data.” This needs to be all hands-on deck. I see things through a different lens than

the clinicians and the actuaries. Usually we have people from the analytics practice who intake,

process, QA, and publish the data. We can go to them with questions. But it’s really people like me and

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clinicians who look through the data. Clinicians can be RNs or Medical Directors, who have a good

vantage point to synthesize the data points. We try to bridge the gap that way. Our group has done a

pretty good job of getting better connected across teams. Because what we are seeing more and more

is that clients want an interdisciplinary team working on doing this.” -Respondent 9

This bidirectional, cross functional collaboration supports another concept found in

the literature: bottom-up communication strategies. According to Côrte-Real et al.

(2017), the bottom up strategy consists of implementing effective communication

throughout the whole organization by involving data users, technical experts, and

communicators together in order to maximize the quality of the gathered data and

consecutively achieve competitive advantage. Respondents shared tactics and

strategies they use to communicate across teams and with practitioners with different

backgrounds, competencies, or skill sets. Respondent 7 explained that this

interconnectivity is essential to the success of the business: “...working with different

kinds of engineering and technical teams, the approach I’ve taken is to just ask a lot

of questions. Being able to have the big picture and being able to dive down deep

enough into the technical to be able to walk away and help that team, or speak about

it, or be able to describe the risks, or challenges, or future needs to be able to set the

product up for success is important.” Respondents shared the importance of cultivating

trusted relationships between diverse groups of employees throughout the

organization. Building internal rapport is essential to employee engagement according

to theory. Cultivating strong relationships as a success factor in all areas of

communications appeared in the findings very frequently. Multiple authors (Bakker,

Albrecht, & Leiter, 2011; Bindl & Parker, 2010; Saks, 2006) consider employee

engagement the cornerstone of an effective corporate communication strategy. This

view brings attention to employees’ knowledge and skills about both their jobs and

the organization (Gronstedt, 2000), absorptive capacity and performance of their roles

(Saks, 2006), as well as, elementary communications strategies to be understood, such

as informal communication, storytelling methodologies, and coaching on complex

matters through useful and tailored messages to employees (Pounsford, 2007). These

internal practices are believed to improve engagement from employees and encourage

trust in the organization, which boosts the company reputation and becomes an

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essential competitive advantage. This theoretical framework is particularly relevant to

the healthcare industry due to the sensitive meaning and terminology of the message,

and the high impact that the interpretation of data has in society. In line with these

theoretical insights, Respondent 6 said, “I’m not a scientist. I’m not a doctor. I’m not

a statistician. But I have always tried to cultivate relationships with those people in the

company and have them explain it to me. I’ve had helpful partners who are very patient

and explain things to me. You have to simplify it. It’s about building trust with your

colleagues and getting their respect.”

The healthcare industry, with highly technical terminology and deeply sensitive

personal health information, means the sender has a very important responsibility to

ensure understanding and interpretation of messages is accurate, not only to advance

business performance but also advance society wellbeing. Respondents indicated that

each function of corporate communications must work in unison together. This

holistic view of corporate communication is integral to the conceptual framework. The

sender includes each component sphere of communications that is responsible for

distributing messages and communicating information derived from big data. It is for

this reason; the research participants emphasized the importance of healthy

relationships and communication across divisions in order to make sure the

interpretation of the information flow is clear and correct towards the external

audience. Respondent 13 provided a very rich comment about internal practices to

strengthen how to communicate data: “I collaborate constantly with the marketing and

social media teams. We rely on each other a lot. We share ideas and stories. The lines

between our functions are so blurry. I think it’s for the good of storytelling because

we each have audiences we need to message to. And it is good for all of us to

understand how the story can be understood through different lenses”.

Respondent 13 also pointed out an important element in alignment with the internal

communications literature: “If our employees aren’t happy, our customers won’t be

happy. An informed employee is an empowered employee. As communicators, we

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need to make sure employees know what they need to know, and know how to

influence where the company is going. How they can be part of the strategy.” From

the internal communication side, this approach validates the learning and knowledge

based view, as for healthcare multinationals, the interrelationships of its employees

(Quirke, 2012) and collaborative organizational culture encourage employee

engagement, which is essential in order to navigate constant and fast-pace

development of the healthcare industry (Mazzei, 2014; Tkalac Verčič & Pološki

Vokić, 2017; Zerfass & Viertmann, 2017; Bailey et al., 2017). According to the

empirical research, communicators maintain a global approach while they adapt to the

local market or a set of similar countries (e.g. the Nordic countries), meaning that there

exists both one way and bidirectional communication with the head office (often

referred to in the interview transcripts as “corporate” or “the global office” or “the

head office”) as well as a datasets owned by the multinational. However, in most cases,

the communication would not happen from communicators of one country to another

without passing through the head office and the information from one country does

not fully apply to a different one. In practice, Respondent 4 explained how

multinational organizations apply a global scope by creating databases across the

countries they are present in but afterwards, they adapt the data to the local market:

“The data that we use primarily in the Nordics is from the national healthcare registers.

They are available in all the Nordic countries… the Nordics are set up to do these

novel data analytics and novel epidemiology studies because of these registers. Very

few countries have this type of total population registers and have had them for so

long. Because it is such a world unique asset, that’s what we are using for our research.

Every country has a different way to capture this real-world health care analytic data.

Each country has their own data assets.” Respondent 5 shared a similar view when it

comes to adapting to the local organization: “We have to say what are the

circumstances and local regulations in each country and then lean on the local trade

organizations to understand if we can distribute this kind of information.”

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Encoding

3 steps to strategically use big data in corporate communication

According to the communication model of Shannon and Weaver (1948), for the sender

to deliver a message to the receiver, the information of the message goes through an

encoding process. Additionally, in the authors’ theoretical model, the encoding

process integrates the theory from Wiencierz and Rötger (2017), in order to

incorporate all of the requirements associated with big data generation in the

communication process. The research participants work daily with data, either to

gather it, analyze it, interpret it, or communicate it, hence, this segment of the

communication process is especially critical. This stage is also when data is

transformed into information. López-Robles (2019) recognizes this transformation as

the outcome of data analytics. The collection, analysis and dissemination of data which

turns it into valuable information and contributes to opportunity identification,

dynamic capabilities and decision-making process of the organization (López-Robles,

2019). This was supported frequently by the research participants. For example,

Respondent 9 describes how crucial this information is for decision making:

“This is where we really use the data. Once we gather all this information from their stakeholders then

we pair it with their data to formulate a conclusion. The best way to persuade the CFO of the health

plan is with data. It has become fundamental to include their data, compare it with other data, and

make sure we have the right level of data in there. The better we get at using data, and creating

benchmarks, and proving there is ROI associated with interventions related to what we find in the data,

the faster we will be able to move as a business and the more trustworthy we will be to our clients.

People trust numbers over opinions.” -Respondent 9

The first step of the Encoding stage within the conceptual framework sets the

objective and communication problem at hand. From the theory and empirical results,

there are many connections to big data challenges. Respondents noted the importance

of establishing the intent of the communication and what the communication seeks to

solve. Moreover, this step helps begin to narrow in on what type of big data will be

useful for the message. Both theories from Wiencierz and Röttger (2017) and

Bumblauskas, et al. (2017) are integrated in this step. Wiencierz and Röttger (2017)

posit that key considerations when utilizing big data are the 4Vs: volume, velocity,

veracity, and variety. This is important because scholars have documented the

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complexity of dealing with large amounts of data and the inherent challenge of making

good use of all the data they have access to. Bumblauskas, et al. (2017) explains the

challenge of harnessing large amounts of data is ultimately about making good use of

data that is available. Often communicators face a “data binge”, which makes the

process of transforming data into information difficult. Data binges risk the value of

data when it is not well managed to begin with. This theoretical approach maintains

data quality over quantity. Respondent 9 agreed with this theoretical perception: “A

struggle for my team is that there is just so much data.” First in terms of volume: “We

spend so much time digging through the data. Summarizing it into one sentence for

what it means for our client is the hardest thing. When there are so many different

people chiming in.” Secondly in terms of variety: “There is so much data in the

healthcare system and so many different ways to look at it and ways to calculate things

and carve the data up”. Then in terms of velocity: “The speed at which data grows is

crazy. What I’m finding is that we have so much data. The issue is not a shortage of

data. We have plenty. The challenge comes when we try to make true sense of it”. And

finally, in terms of veracity of the final outcome: “We have an abundance of data, but

we starve for knowledge. No one is very good at making sense of it.”

Côrte-Real, et al. (2017) presented a similar approach by stressing the importance of

the way companies use what they know, rather than the benefit of constantly

expanding organizational knowledge and information. This is positioned within the

conceptual framework in the second step of the Encoding stage which assesses

whether big data can help. Respondent 7 supporting this theoretical approach: “Before

you build anything, you have to define what and why you’d be collecting it and for

what purpose and for how long you’re going to store it and what happens to it

afterward. “You’re still very careful about what you collect. You basically don’t want

to collect anything unless you have a really valid reason for needing it. You also talk

about what are the bounds of what you want to do with that data. For the things that

aren’t relevant for your product purpose, how do you ensure it won’t be used for those

other reasons.” Additionally, Respondent 2 showed awareness and how the team aims

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at trying to tackle this matter: “My team is trying to be more strategic about where we

get our data and what we have. There is a lot of redundant data sets and we aren’t

being very smart about how we link different sources and how we get to insights by

looking across different data sets.”

The third step of the Encoding stage focuses on the requirements for data generation.

In the attempt to connect the literature review with the empirical results, a common

encounter in the theory was the resource-based view approach, which allocates big

data within the organizational value chain. Under this view, big data is another asset

of the organization, which means that it is dependent on organizational resources such

as financial investment and technology development in order to be a valuable asset for

the company and develop competitive advantages (Côrte-Real et al., 2017). Research

participants explained that big data and information are becoming more important in

organizations and shared how their employers seek experts on big data and

communication of information. In fact, respondents said companies are creating entire

departments and teams dedicated to this matter in order to make the most of the data

and develop valuable information and knowledge for the organization. If companies

cannot hire professionals with these competencies, healthcare MNCs are increasingly

offering employee trainings to cultivate these skillsets. Respondent 1 highlighted the

necessity to integrate soft skills among management and technical teams to coordinate

and create effective communication: “There are different types of business

intelligence roles. There is the insights part where you are doing a lot of analysis and

communicating it to the business. But then there is data analytics, modelling, data

warehouse type. I have found myself to be better at the communication and analysis

rather than the technical side. The technical people don’t always have the soft skills to

communicate effectively. Companies really need to invest in people development and

offer training courses in this area. When you come into a role that demands both

technical and soft skills, it is difficult.” Respondent 2 also confirmed this phenomenon

where their company realized they needed internal communication support: “There

are over 2,800 employees at the company doing data and analytics. All these teams

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were basically operating independently and had no shared strategy. The industry is

changing a lot and is focused on advanced analytics than traditional methods and there

was no central team to get everybody aligned and heading in the same direction. So

that’s how my communications team formed.” Additionally, Respondent 4 underlined

the strength of an interdisciplinary team with an array of skills: “I find that people who

have that strength and can communicate data are super power people. That is really

what is needed. You need a complex team to be able to analyze and communicate this

data. One necessity on the team is to have visual analytics people that are there and

able to show what the message is to communicate the impact.” Much like Respondents

2 and 4, other respondents acknowledged that there is a need for an integrated team

with variety of skill sets, however, they said it is still difficult to hire and difficult to

train current employees. Thus, this is an evolving process.

Message

Transforming data into information

Data analytics is the methodology by which big data is organized, analyzed and

eventually turned into information (Mikalef, et al., 2018; Agarwal et al., 2019).

Following the Rowley (2007) data-information-knowledge-wisdom pyramid

(Rowley, 2007), in order to generate knowledge for the organization, the information

needs to be interpreted and put into context to strategically boost organizational

performance (Chen et al., 2012; Saleem Sumbal, et al., 2017). From a practical

approach, this process can be understood as a work in progress where data is the raw

material and information is the outcome, and depending on the context, prior

experiences and background of the whole team, organizational knowledge is

developed. Research participants explained this process as it tactically occurs in their

day-to-day projects and communications initiatives. For example, Respondent 9 said:

“We have people from the analytics practice who intake, process, QA, and publish the

data. We can go to them with questions. But it’s really people like me and clinicians

who look through the data… synthesize the data points. We try to bridge the gap that

way.”

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Scholars also discussed how big data is not always seen as fully beneficial for

organizations (Côrte-Real, et al., 2017; Bumblauskas, et al., 2017). Handling big data

can be counterproductive due to the large amount and wide variety of data and

information. From this approach, as noted in the Encoding process, the quality of the

data is emphasized over quantity (Bumblauskas, et al., 2017; Aula, 2019; Bates, 2018).

Respondent 2 upholds this notion: “We have so much data from claims data and

electronic health records. We have a lot of data on provider quality data. We use a

third-party company to get data on consumer behavior. We do social determinants of

health and demographics stuff. My team is trying to be more strategic about where we

get our data and what we have. There are a lot of redundant data sets and we aren’t

being very smart about how we link different sources and how we get to insights by

looking across different data sets. In terms of what we build and work on.” It is

difficult to transform data to information that can ultimately be coded into a message

if the data sets are too challenging to manage.

Channels

Mechanism for delivering information

The Berlo (1960) Sender Message Channel Receiver Model is the foundation of the

conceptual framework as it demonstrates the most basic and core tenants of the

communication process. Additionally, it demonstrates the interconnectedness of each

phase in the development of a communication. In the SMCR model, Channel is located

in between the Message and the Receiver, thus, the communication channel can be

interpreted as responsible for the delivery of the information within the message to the

receiver. The purpose of the message can vary depending on its sender. Wiencierz and

Röttger (2017) maintained the importance of differentiating between marketing

communication, public relationships and internal communication, respondents

validated this by demonstrating the need for adaptation depending on the context

(internal or external) and to tailor the channel of the message to the targeted receiver

and their preferences.

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From the marketing communications approach, communication channels in the health

industry do not follow the same promotion practices as traditional buyer/seller

marketing. Since the healthcare industry is a complex ecosystem of stakeholders, the

focus is not only on the end user or the consumer because there are intermediaries and

stakeholders in between (Butt et al., 2019). This makes identifying channels for

effective dissemination of corporate identity, brand, customer, and product-related

communications even more challenging (Wiencierz and Röttger, 2017). Alternatively,

the public relations literature highlights the power of coordinating communication

activities within the organization as well as within the industry (Hasnmeyer and Topic,

2015). Transparency and working in alignment across companies within the industry,

and sharing communication strategies internally is crucial in order to ensure the

organization and the industry communicate a unified message to society (Hasnmeyer

and Topic, 2015), and that valuable information reaches the intended targeted

audience. In regards to internal communication, the theory highlights the benefit of

combining face-to-face and digital forms of communication in order to engage team

members with the priorities of the organization (White, et al., 2010;Welch, 2012;

Stein, 2006; Woodall, 2006). The intent is to establish tailored communication

channels adapted according to the situation and audience preferences. Respondent 1

explained the internal communication channels they use: “Excel spreadsheets,

PowerPoint decks, but there are also data visualization platforms. For example, for

monthly reporting we have an automated tool that stakeholders can access through a

link. There is a lot of focus on digital. We try to move manual reporting tasks to

automated reporting. Developing dashboards and such. This is important because then

the business can access this data whenever they need it and not have to go through

us.”

Additionally, the theory takes into consideration the digital transformation in

communication, which is also affecting the healthcare industry and subsequently its

communication channels. Health-related organizations such as pharmaceuticals,

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health insurers or treatment-clinics are gaining an increasing presence on social media

(Busto-Salinas, 2019), and today, posts from their social media profiles can have a

stronger impact than other traditional communication channels. However, according

to Clair and Mandler (2019), those who adapt to the new communication era while

holding traditional peer-to-peer authentic relationships will reach success (Clair and

Mandler, 2019). This was confirmed in the research participant interviews.

Respondent 12 provided a very rich description of how they combine traditional and

digital in terms of internal and external communication channels:

“Externally we have digital and social. Digital is our website. Social is our social media channels.

Internally we have our Intranet and email. We use meetings and townhalls. We use traditional

advertising. We use executive visibility platforms, that could be a conference where you have one of

your leaders speak as a subject matter expert on a particular topic. You use employees quite frankly.

Employees are a really critical channel for companies nowadays and it then translates back to their

social media. If your employees are comfortable serving as informal ambassadors of your company. If

your company posts something on LinkedIn about how we all went on a walk to raise awareness of

pancreatic cancer and then 500 of your employees repost that or comment or Tweet about it that’s how

you amplify your voice through stakeholders.” -Respondent 12

Decoding

Interpretation of information

The Decoding stage of the conceptual framework represents a critical point along the

communication process. The decontextualization and recontextualization of data,

meaning not over-interpreting or missing essential nuances of the data when

communicating to different audiences, not only across functions but also across

borders, will enhance the quality of the information of the message (Leonelli, 2014;

Aula, 2019; Bates, 2018). The overall capacity to precisely measure and analyze big

data and further create relevant information in accordance to a context, will enhance

the reliability, trust and commitment to communication interactivity (García- Orosa,

2019). Respondent 9 confirmed this and said, “My job is to make sure that the data is

right. Everything is right. We are not mistranslating something from our analytics

teams. We need to make sure there are no errors in how we are communicating it out.”

Likewise, Respondent 4 describes the process as: “We always have to be careful how

we communicate findings from data. They involve advanced mathematical modeling,

they involve advanced disease modeling, they involve aspects that need to be

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communicated in many ways in order to make sense of them. That’s something I

always think about when we are communicating our findings from our data, how are

we simplifying them in ways that people can understand the content but yet not remove

any of the essential parts from the studies. That’s really important. We have to walk

such a fine line. Of course, in a regulated industry we cannot say more than what our

data shows. You always have these sections where you hone in and distill your

message. Scientists are very conservative. You don’t want to over interpret. The thing

with big data analytics is that there is so much complexity in how it’s done.”

5.2 Differences in the existing literature

Receiver

Audiences and stakeholders

Despite consensus between theory and empirical findings, the international challenge

that rises in the health big data field due to the data governance is strongly attached to

the different national regulations and policies (Agarwal et al., 2019; Aula, 2019),

where some authors consider the implementation of global coordinated measures and

the creation of an open database generated by the society, the public and the private

sector a crucial and major development for the global society (Aula, 2019), the

researchers observed that the practice was not on the same line as the theory. It seems

it is strongly assumed by data users and data communications this international barrier

across health systems. It has not been observed a clear demand from the participants

on coordinated methodologies and global open databases, there was a common

understanding in each country having their data sets and regulations for it, strongly

connected to their own needs, health systems, health population and maturity level of

technology, and therefore, organizational strategies coping with that. To date, this

theoretical insight can be seen in practice as a visionary idea for the future. For

instance, Respondent 1 and 4, both working in a multinational pharmaceutical and

biomedicine company, demonstrated their acknowledgement towards it as: “I work

across all four Nordic countries for immunology and they are all so different. If I am

short on data for Norway, I can’t use data from Sweden and apply it to Norway because

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the markets are so different. The data is very specific to the country. There are also so

many regulations for collection of market research data in each country.” Respondent

4 said, “Every country has a different way to capture this real-world health care

analytic data. Each country has their own data assets. Any country with any type of

digital health record system is going to have a data asset.”

Sender

Internal communication, marketing communication and public relations.

The proposed conceptual framework understands the Sender as the big data

communicators, differentiated by the three corporate communication disciplines:

marketing communications, public relations. and internal communication. This

categorization was also followed in the literature review. Yet, empirical observations

did not specifically differentiate in between the three disciplines when talking about

big data communication, which granted a holistic view of corporate communication,

showing the strong influence internal communication has on external communication

and vice versa. This was explicitly distinguished among the research participants,

which disrupts previous business theories, Respondent 1 summarized it as: “It is to

help people internally to make a decision externally. That is the core end goal for

business intelligence.”

Encoding

3 steps to strategically use big data in corporate communication.

Despite the fact that big data intelligence is recognized as a powerful organizational

tool for opportunity identification, dynamic capabilities, decision-making process and

strategic management overall (López-Robles, 2019; Goodman, 2019; Wiencierz and

Röttger, 2017), there are authors (García-Orosa, 2019) who question the capacity of

big data intelligence to accurately provide quality information. The researchers

acknowledged the time and resource investments healthcare organizations are

investing in big data and the entire process of strategically using it in corporate

communication, and yet they are still not able to cope with the fast pace of change as

it develops, which challenges its potential. At the same time, data users and data

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communicators aim and believe in big data without questioning its capabilities, they

experience hard times to follow the rhythm big data develops at, seemingly, due to

lack of not only financial resources but also knowledge or skill sets of their workforce.

Respondent 4 brought his/her eagerness to get to work with data visualization and

artificial intelligence and its potential: “I don’t use it nearly as much as I should. But

we also need to apply data visualization and AI where appropriate. I’ve seen studies

that use AI methodologies where it is junk in junk out. We have to use it well and use

it wisely. Understand where it is applied, where it shouldn’t be applied. What it can

say, what it can’t say. But also, that is the future. It is going to enhance what we can

do. It is going to do brilliant things.” Respondent 9 emphasized the constant effort

required by organizations to strictly follow the fast pace big data technology develops:

“It has taken years. Since I’ve been here, we have come pretty far, but we are nowhere

near where we need to be in making sense of the data. This is the natural evolution of

technology. We’ve built the technology to track all the data, we figured out the big

areas we should be tracking, we’ve started to harness it, but then translating it into

action is what we are trying to figure out now.” Along with Respondent 11, who

expresses the wish to implement data analytics and intelligence, specifically data

visualization, but the lack of financial resources limits it: “Unfortunately, not as much

as I would like. No. It’s basically PowerPoint and Outlook. I would love to have more

sophisticated tools, but at the moment I don’t because I don’t have budget or

resourcing.”

Message

Transforming data into information

The conversion from big data to valuable information for the organization requires

analytics techniques as the DIKW pyramid (Rowley, 2017), and together with other

authors (Friké, 2009; Chen et al., 2012; Salem Sumbal, et al., 2017) acknowledge.

And despite the awareness regarding the big data complexities due to its diversity and

extension, and the ongoing advances on storage, processing and analyzing big data

(Argarwal, et al., 2019); the empirical results show clear evidence of this conversion

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process, yet an unanticipated finding for the researchers was the wide spectrum that

the concept of analytics encompasses in reality. Although the research participants

represented different types of job positions, types of organizations and personal

backgrounds, they were all health-related firms and therefore, health-related data, and

yet they all presented different ways to gather, structure and analyze their data.

Furthermore, research participants emphasized a major element of the analytics

process was based on teamwork, hence cultivating these relationships can have a

major impact on the information outcome. On one hand, Respondent 9 shows his/her

alignment regarding the vast amount and variety of data to be handled. However, as a

consequence, and adding new insights, it is stressed out the importance of back and

forth communication within the team and across departments, in order to justify how

data has been used and processed and why certain methodology and not a different

one: “One thing that is really important and often undervalued. There is so much data

in the healthcare system and so many different ways to look at it and ways to calculate

things and carve the data up. It is so so so critically important that when we talk to

people about data that we set the table on all of the specifications of what we are

looking at. It’s really important for us to say, “Here’s what we looked at. Here’s how

we calculated it. Here’s the difference between how you calculate things versus how

we calculate it. This is the methodology we use.” All of that. That is a big part of

communicating data is laying out all the specs.” Respondent 7 strengthened as well

the value of making sure that everyone in the team involved in the process, understand

the used methodology to turn big data into information: “I won’t go down the full list,

but there are a lot of really important players, and the more you get to know them, and

understand how much heads up is helpful, and where they fit in the overall product

development lifecycle, the easier it is for everyone and sets us up for success if

everyone understands why did we build this, who was it for, why does it matter. That

will be important in messaging the value of it to people.” Decisively, Respondent 2

remarked as well on the added value the output, referring to the information, when

there is a double-sense flow of communication in between the data user and data

communicator: “Output is going to be so much better if you can send a draft that is

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not perfect but you know you’re going to get feedback in a timely manner. When you

can be back and forth, that is when it is a much higher quality output.”

Channels

Mechanism for delivering information

The fast development of digital communication methods makes it difficult for theories

to keep track of all the possible communication channels for organizations and its

effectiveness. Empirical results show that organizations today count on a wide range

of options to communicate either internally or externally with the different audiences.

And from a corporate communication perspective the main observation from the

gathered information, is the capability of adaptation from organizations in order to

maintain an active and effective communication channel by harnessing all the

available possibilities. Although Clair and Mandler (2019) supported the idea of

combining both traditional person-to-person and digital relationships, the current

health crisis is challenging organizations and new communication channels are

playing a critical role, video-calls and conferences held digitally have substituted face-

to-face contact and physical meetings worldwide. The authors experienced this instant

adaptation when the first interviews with the research participant were initially

planned to be held physically, but from one week to another, due to the eventful

situation caused by the pandemic they all ended up being remotely held and successful.

A second popular finding from the empirical results that seems relatively imbalanced

regarding the prior literature, is the value of bidirectional communication in digital-

based channels. The shift in communications has increased the interactivity in between

senders and receivers; and any feedback or characteristic of the interaction within the

communication flow can be interpreted as a source of big data, and once structured

and analyzed, it will deliver information to the organization. Respondent 12 provided

a very insightful composition supporting the value of the feedback organizations get

when using digital communication channels: “Social, digital media is very data rich.

You can tell how long people come to your website, what do they click on, how long

they spend on a page, how many people have read your tweet, how many people have

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retweeted or posted a comment.” Additionally, Respondent 12 also considers that this

way of gathering data can generate excessive volume of data: “It is so significant that

we haven’t figured out what to do with it all.” As well as a wide variety of data, either

from internal or external channels, qualitative or quantitative: “For employees we look

at the number of people who clicked on an email or participated in a webcast. It’s also

both quantitative and qualitative. Even on social media, we will look at are they an

influencer? What is their reach? We will also look at the comments and see what is

the tone of the comments? We do that with the media.” Finally, Respondent 12

compares traditional communication channels with digital ones, and it can be further

acknowledged his/her preference towards digital channels: “In the olden days you

used to count press impressions. If you were on the front page of the New York Times

and they sell to X million people, that means you got X number of impressions.

Nowadays we can dig a little deeper, in terms of the demographics of who reads an

online publication and figure out was this post then put on social media? If it was

posted on social media by a journalist, who retweeted it? Who liked it? There is a way

to extend the data of what traditional metrics could give us.”

Decoding

Interpretation of information

The decoding stage can be perceived close to big data intelligence, since at this

position, it is when the information is applied in context and becomes knowledge

(Rowley, 2007). Quirke (2012) defined knowledge and interrelationships of its people

as major assets for an organization. And the contribution of Pounsford (2007),

underlined storytelling, informal communication and coaching as strategies to

enhance employee engagement and consecutively the organization performance. In

line with organizational theory, these strategies are forwarded to an internal

communication dimension. However, empirical results show the implementation of

training projects and coaching to a wider audience, including external stakeholders, is

a reality that enhances communication, specifically at the decoding step, expanding

engagement of employees to an engagement of a broader audience. Respondent 8, as

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medical advisor from a pharmaceutical multinational, shared an insight where internal

and external practices are carried out with the aim of boosting the information received

by its audience. To begin with, Respondent 8 addressed PowerPoint and poster

presentations for scientific congress, study articles and leaflets as

popular communication to connect with external audiences, stressing the value of an

accurate translation of the information: “PowerPoint presentation is by far the most

common. What I’m working on right now is a poster and writing an abstract for a

scientific congress. You make a big poster summarizing the findings and then stand

there for a few hours and talk to people attending the congress about our studies. I also

write articles summarizing the data. You can provide published scientific articles to

health care professionals. Our big stage 3 studies, we can hand them a copy of that.

The commercial team makes leaflets for nurses and doctors and patients. The medical

information team creates FAQs and provides answers to questions we receive about

our drugs.” Respondent 8 further emphasizes on the adaptation of the message

depending on the audience the message is forwarded, supported by a strong

storytelling strategy depending on the audience: “You have to simplify. Broad strokes.

You can’t go into all the details. You have to be more direct. In scientific articles you

bring up all your arguments and come to a conclusion at the end. But we have to do

the opposite when talking to non-scientific audiences. The degree to which you need

to simplify differs based on your audience. If I’m talking to health care professionals

at a university clinic, they are more research oriented and they are usually pretty

specialized. But in other clinical settings doctors treat many different disease areas so

they aren’t as familiar with the really technical aspects of a drug or a study we have

done related to the drug.” Finally, Respondent 8 also highlights the importance of

internal communication, previous training and coaching in order for an external

communicators to deliver an efficient message to the different target groups: “We do

internal communication where we provide training to make sure sales and commercial

people are knowledgeable and prepared to answer questions when they are out in the

field. This was harder than I thought. We have training materials we receive from

global. There are also web or online courses. We have official trainings and more

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informal meetings we contribute to. The key account managers usually do not have a

medical background. We do role play exercises. Where I pretend to be a doctor or the

customer and ask the sales people difficult questions. It is fun! [laughs] It is helpful to

know what kind of questions sales people get when they are out meeting clients and

then we share what we hear when we are meeting with HCPs too.” Yet, in this industry

there is a thin line about what you can externally communicate and how, as

Respondent 8, stated: “You can't talk about the drug. You can only talk about the

disease.”, which reconnects with data governance.

5.3 Summary of analysis

The intent of the analysis has been to compare the existing literature to the empirical

findings, following the structure of the conceptual framework structure in order to

prove its applicability to the research. The authors were able to identify shared insights

between prior literature and empirical results, as well as differences where theory does

not fully meet empirical practices, or where the empirical findings did not support the

theoretical frameworks. The research sample yielded 13 research participant

perspectives across the pharmaceuticals, medical devices, consumer products, health

technology, and data analytics industries. These interviews offered thorough and in-

depth data and insights that illustrated the current state of corporate communications

in the healthcare industry. Additionally, the literature review provided an

interdisciplinary theoretical view focused on big data application and corporate

communication, both pillars in the healthcare industry. In summary, the empirical

findings replicated many existing theories and concepts from the healthcare

communications and international business literature. However, the findings expanded

existing knowledge in the information science and communication literature by

describing tactically, and strategically, how communicators reach internal and external

audiences while using information derived from big data. The main findings of the

analysis, taking into consideration both the similarities and differences in combining

theory and practice, are associated closely related two elements: first, technological

development has drastically changed communication and organizational management,

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and secondly, the communicating, and especially communicating data, in the

healthcare industry involves a high degree of complexity in content, stakeholder

management, regulatory compliance, and international business. These core findings

will be further discussed in the conclusion in the context of theoretical and practice

implications.

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6 Conclusion

The conclusion brings together all of the findings throughout each chapter of the thesis

and provides answers to the research questions. Next, the authors amend the

conceptual framework to incorporate the empirical findings. Furthermore, the authors

provide theoretical, managerial, policy, social, and sustainability-related implications

derived from this research. Finally, the limitations of this study, as well as future

research opportunities are discussed.

As the analysis chapter demonstrates, the findings from this study confirms previous

research but also illuminates new information about communicating big data in the

healthcare industry. A major early discovery was the lack of literature from

international business and management theory as it relates to big data and its influence

on organizational knowledge and corporate communication. In regard to the area of

corporate communications, as emphasized throughout the existing publications, the

authors intended to address this gap and take a holistic approach in order to understand

each component sphere of corporate communications and the utilization of

information derived from big data. In addition, the research was conducted within the

context of the healthcare industry due to the clear abundance of big data inherent in

the business operations. Ultimately, the intent of this study was to answer the research

questions: 1) how are multinational healthcare corporations communicating

information derived from big data to internal and external stakeholders? 2) What

challenges do communicators in the healthcare industry face when utilizing big data?

6.1 Answers to the research questions

RQ1. How are multinational healthcare companies communicating information

derived from big data to internal and external stakeholders?

The first key finding in response to this question involves how healthcare companies

organize and structure their corporate communications business unit into three distinct

functions: marketing communications, internal communications, and public relations.

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These component spheres of communications are bifurcated based on audience which

is unique to this industry due to the large number of audiences and stakeholders

healthcare companies must communicate with in order to achieve their business

objectives. Both the empirical and theoretical findings demonstrated this divide in

communication responsibilities in order to effectively engage stakeholders (see Table

1). Likewise, as it relates specifically to the research question, how each of these

functions communicates information derived from big data is unique to their

audiences. Both marketing communications and public relations face external

regulatory and legal challenges in communicating which make the process of

integrating big data into their communications more heavily scrutinized so as not to

breach legal protocols. Despite this, data is used heavily in order to promote products

(e.g. efficacy of a medical device or biomedicine). Internal communications is seen as

a platform for testing external messaging. Respondents indicated that often employees

at healthcare companies have some degree of data or scientific awareness, if they are

not already fully credentialized in an adjacent clinical, scientific, or data related field,

so usually they have a higher likelihood of comprehension for complex information.

Thus, if employees do not understand the data jargon, or highly technical information

derived from big data within a message, then it is unlikely other audiences will as well.

Public relations and internal communication also overlap in advancing company-wide

strategy messages. Communicating externally on company successes and

developments is a way of engaging our employees internally. Making progress and

communicating milestones is important for employee engagement and motivation.

Ultimately, the reason the conceptual framework is effective is because it addresses

each component sphere of communication. It is applicable both for internal and

external communication initiatives. Therefore, there were numerous answers to this

research question that applies across all functions of corporate communication.

First, it is essential to have a clear business aim and clear communications objective

when integrating data into messaging. Because the Encoding process is so in-depth

and the Message development is extremely thorough, there must be a clear guiding

motivation, otherwise it is difficult to progress forward. Second, incorporating big

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data into communications requires transforming the data into information and

subsequently into a message(s). The translation of this information requires

meticulous tailoring of the message to the Receiver, which means thoroughly

understanding the audience, including geographic region (e.g. language and

intercultural communication norms), level of education, and experience within the

healthcare field. Third, success factors for communicators in the healthcare industry

include being comfortable working in an environment with a high degree of

complexity. Communicators must have a tolerance for data, science, and technical

content. Another critical success factor for communications practitioners is

relationships within, and across, teams and business units of technical and scientific

practitioners. Building strong networks of subject matter experts enhances the speed

and accuracy of the communications development process. Lastly, internally and

externally communicating data (e.g. clinical trial results, product launches, market

share, sales targets, etc.) related to delivering on business objectives and corporate

strategy drives employee retention, engagement, productivity, and motivation, while

also increasing investor confidence and enhances competitiveness.

RQ2. What challenges do communicators in the healthcare industry face when

utilizing big data?

The challenges associated with communicating information derived from big data in

the health care industry are tied closely with the conceptual framework and can be

categorized based on the stages at which they occur. In the Encoding stage, “bad data”

is a key concern. Bad data can include gaps in the data, missing components of data,

concerns about the validity or veracity of the data, or even data that is simply not

applicable or relevant to the Receiver or audience(s). This is why the Encoding stage

is a three-step process and can be considered rather laborious. It must be meticulous

and involve many parameters in order to mitigate bad data. Likewise, in some cases

empirical and theoretical data advocated for data quality over quantity. However, due

to the highly regulated nature of the healthcare industry quality is always the first

priority, but if there is not enough volume of data, then it may not yield meaningful

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information. Thus, rather than quality over quantity, the adage should be data quality

and quantity.

In the Message stage, translating complex data into information that the Receiver

understands is often a difficult process. For most audiences the translation process

requires simplification. Simplifying complex technical information includes removing

scientific or technical jargon. One way to do this is by daring to ask, “What does this

technical term really mean? What is the data actually showing us?” Another way to

simplify is by using “lay terms,” or language that it is generally understood the

audience will be able to comprehend fully. The most important component is staying

committed to accuracy of the information, maintaining the integrity of the data by not

overstating or understating the information. One of the best ways to ensure accuracy

is to have an iterative drafting process when developing the message. This method

involves the communications practitioner and other key stakeholders, such as the data

expert and legal/compliance functions, reviewing and revising the draft multiple

times.

Throughout the Encoding, Message, and Channel phases, when communicating

externally especially, regulatory requirements are abundant and vary significantly

across countries and regions. This means that when communicating data,

communicators cannot be as agile as practitioners in other industries. The

communication development process is often very labor and time intensive because it

requires meticulous attention to detail. It also requires multiple reviews by a myriad

of stakeholders because the ramifications of errors, making a mistake, or not following

protocol is significant, either financially (due to legal penalties) or reputationally. This

means that sometimes health care MNCs will not even take the risk to communicate

data. Regulation can prevent them from communicating at all.

The Encoding and Message process can be further slowed if communication

practitioners do not have enough data acumen and technical experts do not have

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enough communications skills. Rather than changing communications practitioners

into hybrid data analysts and communicators, creating interdisciplinary teams is the

strongest mechanism for effectively communicating information derived from big

data. The more team members with diverse areas of expertise, competency areas, and

skillsets, the better equipped healthcare MNCs will be in generating, interpreting, and

communicating data. Another way to enhance the communications process is to

provide training for communications practitioners so they can develop a strong science

or data analytics acumen. Likewise, technical experts also need professional

development in effective communications.

6.2 Theoretical Implications

A major contribution to the theory has been the creation of a conceptual framework to

guide the process of communicating big data. This model responds to the research

questions and illustrates how information derived from big data is communicated in

the healthcare industry and attempts to address the challenges communicators face

when working with big data. The conceptual framework (see Figure 4 or Appendix

E for full size model) is based on existing theoretical frameworks and the literature

available on the topic at the initiation of the research process. The authors utilized the

Sender-Message-Channel-Receiver model (Berlo,1960) as the foundation for

demonstrating the core components of big data communication in the healthcare

industry. However, because this model was designed prior to the digital revolution, it

did not include an Encoding process that is relevant or addresses the needs of big data

communication. Encoding is critical to the transformation of data to information.

Audiences will not understand the relevance or impact of the data if it is not modified

into useful or applicable information. The authors also integrated many components

of the Wiencierz and Röttger (2017) framework in order to address the big data

component, however a key element that was missing was the business drivers and

overarching business objectives. The empirical findings maintain the necessity for

contextualizing the communications problem within the business aims otherwise the

communication will not have enough stakeholder buy in to move forward.

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Based on the Analysis and with both the empirical data and the prior theory, the

authors have amended the conceptual framework to reflect the current application of

big data in corporate communications within the healthcare industry.

Figure 5. Communicating big data in the healthcare industry

Although much of the conceptual framework was supported and upheld by the

findings, there were a few areas that illuminated something new or different from what

the authors were able to gather from the literature. Figure 5 (see Appendix H for full

size model) shows an updated conceptual framework with the integration of empirical

data. The revised conceptual framework reflects the communication process flow

healthcare communicators are using when incorporating big data in their

communication strategies. Three amendments were made to the framework. The first

addition was adding a business objective stage, which was included because

respondents said they do not begin developing communications without a clear

business need or directive from the business. The second addition to the framework is

a bi-directional dotted line arrow in between encoding and message, which is intended

to represent the iterative process communicators experience when drafting and editing

messages with information derived from big data. Healthcare is not an industry where

communicators alone can be responsible for content development. It is heavily

matrixed with numerous internal stakeholders involved in the sign off of a

communication. The Message is crafted but then returned back to those involved in

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the encoding process, usually subject matter specialists, data analysts, researchers or

other scientific experts. The final change to the original conceptual framework was

the removal of the numbers indicating which step occurs in which order. In an ideal

scenario, the communications development process would flow in order based on the

framework, but in practicality, this rarely occurs. Respondents explained that

sometimes they initiate the communications process at the Channel stage, Message

stage, or even the Encoding stage. In some instances, the business may want to

optimize a particular channel and draw traffic to other content on that platform, thus

the communicator must work backwards to develop a message for the channel. For

example, in the pharmaceutical industry, medical and scientific congresses are an

important channel for delivering information derived from big data. Companies must

plan for the channel, the congress, rather than plan for a message. In other cases,

communicators start with an existing message. For example, a press release from the

global headquarters has been published and needs to be disseminated through local

channels, so the encoding, identification of the sender, and the business objective do

not need to be determined. Sometimes communications practitioners are not included

in the data generation process at all, and rather, are asked to simply incorporate data

into a communication after the information has been analyzed.

6.3 Managerial implications

This study yielded numerous managerial recommendations. From a training and

development standpoint, because the receiver is such a critical component of the

conceptual frame, it is important for communication practitioners to understand the

highly complex healthcare ecosystem of stakeholders. Likewise, they must have a

baseline understanding of the various country and regional regulatory requirements as

this is necessary in nearly every phase of the communication development process and

the consequences are great if their protocols are violated. Additionally, training in

basic data, analytics, or scientific terminology, processes, and methodology would

enhance data communications in the heathcare industry. Similarly, subject matter

experts could enhance the communications process by improving their communication

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skills. Five of the 13 respondents indicated that they participated in or led trainings of

this nature, so it is evident that this need is being recognized in practice. Ongoing

training on digital technologies, tools, and channels would support practitioners in

reaching their audiences as effectively as possible. Ultimately, there is a need for more

practitioners who can function as a bridge between traditional corporate

communications and data science. Ideally managers should recruit and build out

integrated teams with individuals who have a wide variety of skillsets and areas of

expertise.

6.4 Policy, social and/or sustainability implications

Maybe more so than any other industry in international business, the healthcare sector

is thoroughly rooted in the legislative, regulatory, and political arena. This means the

implications of this study from a policy standpoint can be significant. The better

healthcare companies can leverage their data to communicate more effectively with

lawmakers, the more likely they will be to collaborate more effectively and advance

their business priorities in regulatory scenarios. As one of the core tenants of big data

is velocity, data accumulates and grows at a rapid speed, which means healthcare

professionals can derive meaningful information faster than ever before, which

subsequently can be delivered to lawmakers to impact patients and improve health

care systems. Many respondents mentioned how proud they are to work in a business

environment that also has the potential to transform society by curing disease and

improving quality of life for people around the world. This impact is demonstrated in

scientific transparency. For example, many pharmaceutical companies commit to

publishing data/findings from all of their studies, regardless of whether or not the

outcome supports their business goals/objectives but just for the greater good of

science. In the past, these industries have been vilified for being more focused on profit

than taking care of patients. However, communicators who are trained in effectively

messaging data can better convey the impact that the industry is making in meeting

medical needs. Respondent 4 illustrated this phenomenon: “Just because we develop

a new drug that is lifesaving, and it’s approved by the FDA and EMA, doesn’t mean

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that patients are going to get access to it. The studies that we are doing help show the

unmet medical need and this is of value to the payor, and society. If the health care

authorities say they see the value of this innovation. Because we have value-based

medicine in our society, these studies show the value of our medicines.” In order to

sustain research and development and future technological innovation to care for

future health needs, they need approvals from regulatory authorities. They cannot sell

medical devices or healthcare services or pharmaceuticals without governing bodies

understanding their impact to patient lives. This further demonstrates a need for

sophisticated data communications.

6.5 Limitations

The speed of change around how big data evolves and grows is always going to be a

research limitation. How practitioners were utilizing big data at the time of data

collection could change and be considered inaccurate by the time the thesis is

published. However, this simply demonstrates the necessity for scholars to continue

to study this field because as it evolves more questions will emerge. Another

significant limitation in this study was current events. This topic was selected in

December 2019. At that time, healthcare companies, and the international business

environment in general, was operating in a state of “business as usual.” However, by

the time the authors were gathering data, the landscape had changed due to the

coronavirus pandemic and healthcare companies were operating in crisis

circumstances. This made it especially difficult to find research participants. In fact,

some research participants said that if this were normal circumstances they would meet

for an interview, but given the state of affairs, they could not participate. Likewise,

even those who did participate were often short on time and could only meet briefly.

This also meant that the research participants who were willing to take part in an

interview tailored many of their responses to the crisis, rather than their usual business

circumstances. In order to glean a more comprehensive understanding of this topic, it

would be useful to replicate this study during a non-crisis period. Respondents

frequently told us that their normal responsibilities or day-to-day tasks were put on

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hold and they were exclusively focused on COVID-19 response. This certainly

impacted the findings. This does not discredit the findings or call into question the

accuracy of responses, but it is an important context nonetheless.

Another limitation is the size of the healthcare industry, which is an enormous business

sector. The authors intentionally sought out healthcare communicators from a variety

of business areas and industry sectors within healthcare. However, they acknowledge

13 interviews is not representative of the entire industry. In future studies, in order to

enhance the credibility and validity of the findings, and better understand each sector

specifically, the focus could narrow further into just one sector of the healthcare

industry, such as pharmaceuticals, medical devices, consumer products,

biotechnology, or healthcare data analytics.

6.6 Suggestions for further research

In line with the last limitation mentioned above, the fast pace of the current crisis

situation opens a window of new suggestions for further research including how the

pandemic has influenced each business sector and their usage of big data. There is

now also even more of a need to study global healthcare systems and how they

exchange and manage big datasets as well as communication, cooperation, and inter-

industry relationships. From the corporate communication approach, the term

“infodemic” emerged in the past few months and should be examined in the context

of big data as well, including its evolution, comparing the pre COVID-19 times to the

post COVID-19 times can deeply contribute to the research community, businesses

and society as a whole. Additionally, nearly every research participant said they need

to utilize data visualization and artificial intelligence tools more frequently. This is an

area that could expedite the very labor-intensive encoding process in the conceptual

framework and requires more scholarly attention. As a whole, there was an abundance

of research in information systems and applied engineering, but far fewer studies

published in communication sciences, business and organization theory, as well as

sociology, psychology, and anthropology. This is an interdisciplinary topic and all of

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these fields must continue to examine big data in the context of the healthcare industry

more thoroughly.

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7 References

Abraham R., Schneider J., Vom Brocke J. (2019) ‘Data Governance: A conceptual

framework, structured review, and research agenda’. International Journal of

Information Management, Vol. 49, pp. 424-438.

Akter S., Bandara R., Hani U., Fosso-Wamba S., Foropon C., Papadopoulos T. (2019).

‘Analytics-based decision-making for service systems: A qualitative study and agenda

for future research’. International Journal of Information Management, Vol. 48, pp.

85-95.

Agarwal, R., Dugas, M., Gao, G., and Kannan, P. K. (2020) ‘Emerging technologies

and analytics for a new era of value-centered marketing in healthcare’. Journal of the

Academy of Marketing Science, Vol. 48(1), p.9(15).

Aula, V. (2019) ‘Institutions, infrastructures, and data friction – Reforming secondary

use of health data in Finland’. Big Data & Society, Vol. 6(2).

Bakker, A. B., Albrecht, S. L., and Leiter, M. P. (2011). ‘Key questions regarding

work engagement’. European Journal of Work and Organizational Psychology, 20(1),

4-8.

Bates, J. (2018) ‘The politics of data friction’. Journal of Documentation, Vol. 72(2),

pp. 412-429.

Barbaro, M. (2020). ‘A Virus’s Journey Across China’. [online] Nytimes.com.

Available at: https://www.nytimes.com/2020/01/30/podcasts/the-

daily/coronavirus.html?showTranscript=1 [Accessed 2 Feb. 2020].

Berchick, E. R., Barnett, J., and Upton, D. (2019). ‘Health Insurance in the United

States: 2018 - Visualizations.’ [online] Available at:

https://www.census.gov/library/visualizations/2019/demo/p60-267.html [Accessed

April 2020].

98(110)

Berlo, D. (1960). ‘The process of communication: An introduction to theory and

practice’. Holt, Rinehart, and Winston, NY.

Beyer, M.A. and Laney, D. (2012), ‘The importance of ‘big data’: a definition’.

Gartner Research Report, Stamford, CT.

Bindl, U. K., and Parker, S. K. (2010). ‘Feeling good and performing well?

Psychological engagement and positive behaviors at work’. Handbook of employee

engagement: Perspectives, issues, research and practice, Vol. 385.

Bovée, C.L., Thill, J.V. (1992). ‘Study guide to accompany Marketing’. Business

Communication Today. Ny, Ny: McGraw-Hill, 332 p.

Browne, J., 2005.’ Presidential address Commemorating Darwin’. The British Journal

for the History of Science, 38(3), pp.251–274.

Bumblauskas, D., Nold, H., Bumblauskas, P., and Igou, A. (2017) ‘Big data analytics:

transforming data to action’. Business Process Management Journal, Vol. 23, No. 3,

pp. 703-720.

Burrill, S. (2019). US Health Care Leader. Deloitte Insights - 2020 US and global

health care outlook. Laying a foundation for the future. [online] Deloitte.com.

Available at: https://www2.deloitte.com/us/en/pages/life-sciences-and-health-

care/articles/global-health-care-sector-outlook.html [Accessed 2 March 2020].

Buytendijk, F. and Heiser, J. (2013). ‘Confronting the privacy and ethical risks of Big

Data’. Financial Times. London, 24 set. [online] Available at:

https://ft.com/content/105e30a4-2549-11e3-b349-00144feab7de [Accessed

March 2020].

Burnett, M. J. and Dollar, A. (1989). ‘Business Communication: Strategies to

Success’. Dame Publications.

99(110)

Busto-Salinas, L. (2019). ‘Healthcare and social networks: Which organizations are

more active and with which does the public interact more?’. El profesional de la

información, Vol. 28, Issue 2, Pp. 1-10.

Butt, I., Iqbal, T., and Zohaib, S. (2019) ‘Healthcare marketing: A review of the

literature based on citation analysis’, Health Marketing Quarterly, 36:4, 271-290.

Callahan, J. L. (2014) ‘Writing Literature Reviews: A Reprise and Update’. Human

Resource Development Review, 13(3), pp. 271–275. doi:

10.1177/1534484314536705.

Caron, C. (2019). Facebook Announces Plan to Curb Vaccine Misinformation.

[online] Nytimes.com. Available at:

https://www.nytimes.com/2019/03/07/technology/facebook-anti-vaccine-

misinformation.html [Accessed 2 Feb. 2020].

Chen, H., Chiang, R. H. L., and Storey, V. C. (2012). ‘Business Intelligence and

Analytics: From Big Data to Big Impact’. Special Issue: Business Intelligence

Research. MIS Quarterly, Vol. 36, No. 4, pp.1165-1188/December 2012.

Clair, A., and Mandler J. (2019) ‘Building relationships with the new media in a cyber

landscape’. Journal of business strategy, Vol. 40, No. 6, pp. 49-54.

Corbin, J. and Strauss, A. (1990) ‘Grounded Theory Research: Procedures, Canons,

and Evaluative Criteria’. Qualitative Sociology, Vol. 14, No. 1.

Côrte-Real, N., Oliveira, T., Ruivo, P. (2017). ‘Assessing business value of Big Data

Analytics in European firms’. Journal of Business Research, 70 (2017) 379-390.

Dash, S., Shakyawar, S., Sharma, M., Kaushik, S. (2019), ‘Big data in healthcare:

management, analysis, and future prospects’. Journal of Big Data, Vol. 6, No. 54

100(110)

De la Torre, I., García-Zapirain, B., and López-Coronado, M. (2017). ‘Analysis of

Security in Big Data Related to Healthcare. Journal of Digital Forensics, Security and

Law, Vol. 12, No. 3, Art. 5.

Dogramatzis, D. (2002). ‘Pharmaceutical Marketing. A Practical Guide’. Taylor &

Francis.

Ekekwe, N. (2018). ‘How New Technologies Could Transform Africa´s Health Care

System’. [online] Harvard Business Review. Available at:

https://hbr.org/2018/08/how-new-technologies-could-transform-africas-health-care-

system [Accessed 2 March. 2020].

Elgendy, N., and Elragal, A. (2016) ‘Big data analytics in support of the decision-

making process’. Procedia Computer Science, Vol. 100, 1071-1084.

European Commission, (2018). ‘Communication from the Commission to the

European Parliament’. [online] Available at:

https://ec.europa.eu/transparency/regdoc/rep/1/2018/EN/COM-2018-233-F1-EN-

MAIN-PART-1.PDF [Accessed March. 2020]

European Union , Supporting public health in Europe [online] Available at:

https://europa.eu/european-union/topics/health_en [Accessed March. 2020]

Frické, M., (2009). ‘The Knowledge Pyramid: the DIKW Hierarchy’. Journal of

Information Science, Vol. 35(2), pp.135-142.

Gallup (2012). ‘Employee engagement’. [online] Available at:

http://www.gallup.com/consulting/52/employee-engagement.aspx [Accessed March

2020].

García-Orosa, B. (2019) ‘25 years of research in online organizational

communication. Review article’. El profesional de la información. Vol. 28, No. 5.

e280517.

101(110)

GDPR (2016). ‘Regulation (EU) 2016/679 of the European Parliament and of the

Council of 27 April 2016 on the protection of natural persons with regard to the

processing of personal data and on the free movement of such data, and repealing

Directive 95/46’, Official Journal of the European Union (OJ), 59, pp. 1–88.

Goodman, M. B. (2019). ‘Introduction to the special issue: corporate communication

- transformation of strategy’. Journal of Business Strategy, Vol. 49, No. 6, pp. 3-8.

Goodman, P. (2020). SARS Stung the Global Economy. The Coronavirus Is a Greater

Menace. [online] Nytimes.com. Available at:

https://www.nytimes.com/2020/02/03/business/economy/SARS-coronavirus-

economic-impact-

china.html?action=click&module=Top%20Stories&pgtype=Homepage [Accessed 3

Feb. 2020].

Gronstedt, A. (2000). ‘The customer century: Lessons from world-class companies in

integrated marketing communication’. New York, NY: Routledge.

Gupta, D. & Rani, R., (2018). ‘Big Data Framework for Zero-Day Malware

Detection’. Cybernetics and Systems, 49(2), pp.103–121.

Hasenmeyer, V., and Topic, M. (2015). ‘The impact of public relations on the

pharmaceutical industry: A case study of living like you campaign’. Journal of

Medical Marketing, Vol. 15 (3-4) 58-68.

Hersh W. (2014) ‘Health Informatics: Practical Guide for Healthcare and Information

Technology Professionals’. Healthcare Data Analytics, 6th Ed. Pensacola, FL.

IBM (2015). ‘The four V’s of big data’. IBM Big Data & Analytics Hub. [online]

Available at: www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-

big-data.jpg [Accessed February 2020].

102(110)

Kelleher, T. (2001). ‘Public relations roles and media choice’. Journal of Public

Relations Research, 13(4), 303-320.

Kotler (2000) [online] Available at:

https://www.kotlermarketing.com/phil_questions.shtml [Accessed April 2020].

Kayyali, B., Knott, D. & Van Kuiken, S. (2013). ‘How big data is shaping US health

care’. The McKinsey Quarterly, (2), p.17.

Kumar, R. (2011). ‘Research Methodology: A step-by-step guide for beginners’. Sage

Publications, 3rd Ed.

Lamph S. (2012). ‘Regulation of medical devices outside the European Union’.

Journal of the Royal Society of Medicine, 105 Suppl. 1(Suppl 1), S12–S21.

Leonelli, S. (2014) ‘What difference does quantity make? On the epistemology of Big

Data in biology’. Big Data & Society, Vol. 1 (1) pp. 1-11.

Leonidou, L.C., and Theodosiou, M. (2004), ‘The export marketing information

system: An integration of the extant knowledge’. Journal of World of Business, Vol.

39 No. 1, pp. 12-36.

Lineaweaver, N. (2019). Business Insider - The US home healthcare report: How the

healthcare industry is tapping into the booming home care market in 2020. [online]

Available at: https://www.businessinsider.com/us-home-healthcare-

market?r=US&IR=T [Accessed 2 March. 2020].

Loebbecke, C. and Picot, A. (2015), ‘Reflections on societal and business model

transformation arising from digitization and big data analytics: a research agenda’.

The Journal of Strategic Information Systems, Vol. 24 No. 3, pp. 149-157.

López-Robles, J.R., Otegi-Olaso, J.R., Porto Gómez, I., and Cobo, M.J (2019) ‘30

years of intelligence model in management and business: A bibliometric review’.

International Journal of Information Management 48 (2019) 22-38.

103(110)

Luo J., Wu M., Gopukumar D., Zhao Y., (2016). ‘Big Data Application in Biomedical

Research and Health Care: A Literature Review’. Biomedical Informatics Insights

2016:8 1–10.

Macey, W.H. & Schneider, B., (2008). ‘Engaged in Engagement: We Are Delighted

We Did It’. Industrial and Organizational Psychology, 1(1), pp.76–83.

Malkani, R. and Torgerson, T. (2020). Coronavirus a Challenge for China’s Economy.

[online] DBRS Morningstar. Available at:

https://www.dbrs.com/research/356311/coronavirus-a-challenge-for-chinas-economy

[Accessed 3 Feb. 2020].

Mayer-Schönberger, V. and Cukier, K., (2013). ‘Big data : a revolution that will

transform how we live, work, and think’. London: John Murray Publishers.

Merriam, S. B., & Tisdell, E. J. (2015). ‘Qualitative research: A guide to design and

implementation ‘. (4th ed.). San Francisco, CA: Jossey-Bass.

Miah, S. J., Vu, H. Q., Gammack, J., McGrath, M. (2017) ‘A big data analytics method

for tourist behaviour analysis’. Information & Management, Vol 54, 771-785.

Micu, A.C., Dedeker, K., Lewis, I., Moran, R., Netzer, O., Plummer, J. and Rubinson,

J. (2011), ‘Guest editorial: the shape of marketing research in 2021’, Journal of

Advertising Research, Vol. 51 No. 1, pp. 213-221.

Mikalef, P., Pappas, I., O., Krogstie, J., Giannakos, M., (2018). ‘Big data analytics

capabilities: a systematic literature review and research agenda’. Information System

E-Business Management (2018) Vol. 16, 547-578.

National Institute of Standards and Technology (2018).

Ostherr, K., Borodina, S., Bracken, R. C., Lotterman, C., Storer E., and Williams, B.

(2017). ‘Trust and privacy in the context of user generated health data’. Journal of Big

Data & Society, Vol. 4, No. 1.

104(110)

Oxford Economics. (2020). Coronavirus outbreak prompts downward GDP revision.

[online] Available at: https://www.oxfordeconomics.com/my-

oxford/publications/537329 [Accessed 3 Feb. 2020].

Papanicolas, I., Woskie, L.R. & Jha, A.K. (2018). ‘Health Care Spending in the United

States and Other High-Income Countries’. JAMA, 319(10), pp.1024–1039.

Pheage, T. (2017). ‘We can improve health system in Africa’. United Nations Africa

Renewal. [online] Available at:

https://www.un.org/africarenewal/magazine/december-2016-march-2017/we-can-

improve-health-systems-africa [Accessed 3 March. 2020].

Pizam, A and Mansfeld, Y. (2009) ‘Consumer behaviour in travel and tourism’.

London: Taylor and Francis Group.

Pounsford, M. (2007). ‘Using storytelling, conversation and coaching to engage’.

Strategic Communication Management, Vol. 11(3), 32-35

Ransbotham, S., Kiron, D., and Prentice, P. K. (2016). ‘Beyond the hype: The hard

work behind analytics success’. MIT Sloan Management Review, Vol. 57.

Remenyi, W., Money, A. and Swartz, E. (2005) ‘Doing Research in Business and

Management: An Introduction to Process and Method’. London: Sage Publications.

Ritchie J, Lewis J. (2003) Qualitative research practice: a guide for social science

students and researchers. London: Sage.

Roser M. and Ritchie H. (2015). ‘Technological Progress’. Published online at

OurWorldInData.org. : 'https://ourworldindata.org/technological-progress' [Accessed

7 Feb. 2020].

Romm, T. (2020). ‘Facebook, Google, and Twitter scramble to stop misinformation

about coronavirus’. [online] The Washington Post. Available at:

105(110)

https://www.washingtonpost.com/technology/2020/01/27/facebook-google-twitter-

scramble-stop-misinformation-about-coronavirus/ [Accessed 2 Feb. 2020].

Rowley, J. (2006). ‘The wisdom hierarchy: representations of the DIKW hierarchy’.

Journal of Information Science, 33(2), pp.163-180.

Russom P. (2011) ‘Big data analytics’. TDWI Best Practices Report, Fourth Quarter

1–35

Sainz G. (2015). ‘Big Data, Big Value, and the Relevance of Moore's Law’. WW

Social Strategy, Storage Digital Marketing at IBM.

https://www.linkedin.com/pulse/big-data-value-relevance-moores-law-guillermina-

sainz/ [Accessed on 7 Feb. 2020].

Saks, A. M. (2006). ‘Antecedents and consequences of employee engagement’.

Journal of Managerial Psychology, Vol. 21, 600-619

Stein, A. (2006) ‘Employee Communications and Community: An Exploratory

Study’. Journal of Public Relations Research, Vol 18 n. 3, pp. 249-464

Sumbal, M.S., Tsui, E. and See-to, E.W.K. (2017). ‘Interrelationship between big data

and knowledge management: an exploratory study in the oil and gas sector’. Journal

of Knowledge Management, 21(1), pp.180–196.

Shannon, C. E. and Weaver, W. (1963). The Mathematical Theory of Communication.

University of Illinois Press.

Tham, T. Y., Tran, T. L., Prueksaritanond, S., Isidro, J. S., Setia, S., & Welluppillai,

V. (2018). ‘Integrated health care systems in Asia: an urgent necessity’. Clinical

interventions in aging, 13, 2527–2538. https://doi.org/10.2147/CIA.S185048

Todor R. D. and Anastasiu C. V. (2018). ‘A Future Trend in Healthcare: The use of

Big Data’. Bulletin of The Transilvania University of Brasov. Series V: Economic

Sciences, Vol. 11(60) No. 1-2018.

106(110)

United Nations, Global Issues, Big Data for Sustainable Development (2016). [online]

Available at: https://www.un.org/en/sections/issues-depth/big-data-sustainable-

development/index.html [Accessed on 28/01/2020]

Valjak, A., and Draskovic, N. (2011). ‘A Literature Review of Public Relations in

Public Healthcare’. International Journal of Management Cases, Vol. 13, Issue 3, pp.

251-260.

Van Riel C., and Fombrun, C. J.(2007). ‘Essentials of Corporate Communication:

Implementing Practices for Effective Reputation Management’. Routledge, pp.328.

Verčič, D. and Zerfass, A., 2016. ‘A comparative excellence framework for

communication management’. Journal of Communication Management, 20(4),

pp.270–288.

Welch, M. and Jackson, P.R. (2007). ‘Rethinking internal communication: a

stakeholder approach’. Corporate Communications: An International Journal, 12(2),

pp.177–198.

Welch, M., 2011. ‘The evolution of the employee engagement concept:

communication implications’. Corporate Communications: An International Journal,

16(4), pp.328–346.

Welch, M. (2012). ‘Appropriateness and acceptability: Employee perspectives of

internal communication’. Public Relations Review, 38(2), pp.246–254.

White, C., Vanc, A., & Stafford, G. (2010). ‘Internal communication, information

satisfaction, and sense of community: The effect of personal influence’. Journal of

Public Relations Research, 22(1), 65-84.

Wiencierz, C. and Röttger, U. (2017). ‘The use of big data in corporate

communication’. Corporate Communications: An International Journal, Vol. 22 No.

3, pp. 258-272.

107(110)

Woodall, K. (2006). ‘The future of business communication. The IABC Handbook of

Organizational Communication: A Guide to Internal Communication’. Public

Relations, Marketing and Leadership, Jossey-Bass/John Wiley, San Francisco, CA,

514-531.

World Health Organization, (2016). ‘Integrated care model: an overview’. [online]

Available at:

http://www.euro.who.int/__data/assets/pdf_file/0005/322475/Integrated-care-

models-overview.pdf [Accessed 2 March. 2020].

World Economic Forum. (Feb 2015) ‘A brief history of big data everyone should

read’. [online] Available at: https://www.weforum.org/agenda/2015/02/a-brief-

history-of-big-data-everyone-should-read [Accessed on 27/01/2020].

World Economic Forum. (2012) ‘Big Data, Big Impact: New Possibilities for

International Development’. Vital Wave Consulting.

Zerfass, A., Moreno, A., Vercic, D., Verhoeven, P., Wiesenberg, M., and Fechner, R.

(2018). European Communication Monitor. [online] Available at:

https://www.communicationmonitor.eu/2018/06/13/ecm-european-communication-

monitor-2018/ [Accessed April. 2020].

108(110)

Appendix A

109(110)

Appendix B

Moore's Original Graph: The number of Components per Integrated Functions, Intel -

1965. (Roser & Ritchie, 2015)

Moore's Law - Exponential technological progress (Roser & Ritchie, 2015)

110(110)

Appendix C

Interview guide

Note: Prior to beginning the interview, the research participant is given the option to

accept or decline the interview being recorded. The interviewers then explained that

the interview is entirely confidential and any references to company names, colleague

names, or product names will be redacted from the transcripts and kept anonymous.

The research participant’s name will not be used or recorded in the transcript or in the

final report.

Background

1. Can you tell us about your education and professional background?

Current role and responsibilities

2. What is your current role/position?

3. What industry is your company part of?

4. What department are you part of?

5. What area of the business do you support? (e.g. unit and geographic location)

6. What types of communications are you creating (e.g. communications channels,

deliverables, platforms, etc)?

7. Are you utilizing RPA, AI, or data visualizations tools to communicate data? If so,

how?

8. Who are your stakeholders (who is the audience)?

Data and communications

9. When communicating data, what are the communication objectives/goals?

A. What communications problem are seeking to solve?

10. What type of data do you use in your role?

11. How do you determine which datasets to use and communicate?

A. How do you assess the datasets for variety, volume, velocity, veracity?

12. How do you use it? (To describe, predict, diagnose, or make recommendations?)

13. What has been the outcome or impact of using data in communications?

14. How do you measure the impact of your communications?

15. What are the challenges in communicating data?

16. Do you use these communications across regions and international markets?

A. What are the challenges in communicating data across regions and

international markets?

B. What adjustments are required or what factors are taken into consideration

when communicating data across countries?

17. Are these communications shared or utilized across business units, functions, and

departments?

Conclusion

18. What value do your communications add to achieving business objectives?

1(2)

Appendix D

Research participants

RESEARCH

PARTICIPANT JOB TITLE INDUSTRY TYPE BUSINESS AREA

GEOGRAPHIC

SCOPE

YEARS IN

HEALTHCARE

INDUSTRY

EDUCATION

1 Senior Business Insights Analyst Pharmaceuticals Multinational Business intelligence Nordics 4 years Bachelor's degree in Chemistry

Master's degree in Chemistry

2 Communications Specialist Healthcare data analytics Multinational Internal communications United States 4 years Bachelor's degree in Strategic Communications

Master's degree in Healthcare Communications

3 Senior Business Analyst Healthcare data analytics Multinational Data communications United States 4 years Bachelor's degree in Communications Studies

4 Director of Real World Evidence Pharmaceuticals Multinational Medical affairs

Public affairs Global 13 years

Bachelor's degree in Sociology

Bachelor's degree in Nursing

PhD in Epidemiology

5 Head of Communications and Public Affairs Pharmaceuticals Multinational

Public relations

Product communications

Internal communications

Public affairs

Nordic region 11 years Bachelor's degree in Economics

Certificate in Media and Communications

6 Director of Product Communications Pharmaceuticals Multinational

Product communications

Internal communications

Public relations

North America 20 years Bachelor's degree in History

Master's degree in Business Administration

7 Product Manager Biotechnology and

medical devices Multinational Product development Global 12 years

Bachelor's degree in Cognitive Science

Master's in Business Administration

8 Medical Advisor Pharmaceuticals Multinational Medical affairs

Population health research Sweden 13 years

Bachelor's degree in Chemistry

PhD in Neuropharmacology

9 Senior Business Analyst Healthcare data analytics Multinational Data communications United States 4 years Bachelor's degree in Human Resources

10 Head of Communications and Public Affairs Pharmaceuticals Multinational Internal communications

Public relations

Europe, Middle

East, and Africa 14 years

Bachelor's degree in English

Master's degree in English

11 Brand and Communications Manager Biotechnology and

medical devices Multinational

Internal communications

Public relations

Corporate social

responsibility

Brand

Nordics 1 year

Bachelor's degree in Language Studies

Certificate in Executive Communication

Management

12 Vice President of Communications Pharmaceuticals Multinational

Internal communications

Public relations

Corporate social

responsibility

Patient advocacy

North America 23 years Bachelor's degree in Education

Certificate in Public Relations Management

13 Head of Communications Biotechnology and

medical devices Multinational

Internal communications

Public relations Global 1 year Bachelor's degree in Communications

2(2)

Appendix E

Conceptual framework

1(1)

Appendix F

Research participant consent form

Consent form for taking part in thesis interview

By signing this consent form, you approve that your personal data is processed

within the frame of the thesis/study. You can withdraw your consent at any time

by contacting one of the contact persons below. In that case, your personal data

will not be saved or processed any longer without other lawful basis.

The personal data that will be collected from you is the transcript and recording

of the interview. Your personal data will be processed between March-June

2020 and after this the data will be deleted.

You always have the right to request information about what has been registered

about you and to comment on the processing of the data that has been collected

by contacting one of the contact persons below or the higher education

institution’s personal data ombudsman on dataskyddsombud@lnu.se.

Complaints that cannot be solved in dialogue with Linnaeus University can be

sent to the Swedish Data Protection Agency.

……………………………… ………………………………

Signature City and date

………………………………

Name in block letters

Contact information:

Student’s name: Elizabeth Johnson

Student’s email address: ej223gg@student.lnu.se

Student’s name: María Castaño

Student’s email address: mc222se@student.lnu.se

Supervisor’s name: Selcen Öztürkcan

Supervisor’s email address: selcen.ozturkcan@lnu.se

1(2)

Appendix G

Research participants’ stakeholders

Internal stakeholders External stakeholders

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1 x x x x x

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5 x x x x x x x x x

6 x x x x x

7 x x x x x x x

8 x x

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10 x x x x x x x

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12 x x x x x x x x x

13 x x x x

2(2)

Appendix H

Amended conceptual framework